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By Jascha Brinkmann

Thanks to Hiten Shah who generously reviewed an early draft of this article.
Some day at the end of last year I stumbled over a post by Justin Jackson about reducing startup costs.
And it made me think hard about our number one expense:

Email Marketing Software

Over the past few years me and my wife have build a brand new Blog from zero to almost 150.000 email subscribers.
And by the end of last year email marketing had become by far the largest recurring expense in our business.
We were spending 10x as much on Emails than on anything else.
The worst was that costs grew at the same rate we were growing:
Adding about 10.000 new email subscribers each month meant that even if we regularly removed no longer engaged subscribers from our list, the amount we were spending was still increasing way too fast to be healthy.
Especially taking in mind that our revenue doesn’t necessarily grows as fast as your email list.
Our main target market are Spanish speaking countries and only a small number of email subscribers actually end up producing revenue. And the typical revenue per customers is not as high as in other economies typical software prices are calculated on.
So this is what made me thinking:

What would I have to do in order to cut this expense?

Unfortunately the decision was taken from me almost right after new years eve.
I got an email from our Email Marketing Provider that they were increasing prices on a 12 days notice:
The worst thing was that they didn’t even mention how much prices would be increasing.
Instead they decided to promote switching to yearly pricing.
Which meant we were practically pressured into forking over almost 5 figures on a two weeks notice as “the only way” to keep our current pricing.
After some investigating and seeing an outburst of other loyal customers we found out that prices would be at least doubling.
What I didn’t know at that time was that while prices were increasing, sending limits were actually decreased. Meaning that we would end up paying more for sending considerable less emails.
In the end it came down to an almost 300% price increase just for us.
In concrete numbers it would have meant about 1,600$ per month.
And just as Justin Jackson says in his Article:
Which solopreneur couldn’t use an additional 20,000$ per year of guaranteed profit?

Why we didn’t move to a different provider and decided to build our own App instead

Over the past few years we have switched email marketing providers three times and out of experience I can tell you it’s always a major pain.
I loved our previous provider and their functionality but I was seriously disappointed by how they treated us and other customers.
They had just completely lost my trust.
And while Email Marketing is our number one expense its as well the most important part of our content based business.
It’s something we literally can’t live without.
After such a devastating experience I just didn’t feel comfortable giving up again complete control over such an important part of our business to yet another company.
Email Deliverability itself was another important consideration.
With most other Email Marketing Providers you have no control over which emails get sended out together with yours. According to Mailchimp keeping delivery rates high is actually one of the major challenges.
By building our own solution it was fairly simple to have dedicated servers for sending our own emails and have real time information about how exactly we are doing:
We could even do things like separate email sending by highly engaged subscribers and lower engaged subscribers to make sure that the ones who matter most are guaranteed to receive our emails.
Beside that it was a simple economical calculation:
At 1,600$ per month and a 10% increase every other month we would be spending nearly 300,000$ over the next five years:
(Taking in mind that we’ve already spent close to 30,000$ in the past two years)
If it took me just a month of my own time to build a viable solution then we would have a positive ROI within the first year.
And that would only compound over the next five years and expected lifetime of the App.
Having complete control over features and building something truly unique to our advanced needs (more about that in a minute) was just an added bonus.
Beside that:
It was an exciting challenge and real world project that would immediately benefit us where I could apply everything I have learned about building software in the past year.
So for us this just felt like the right decision, but it probably isn’t for a lot of other businesses with lower volume and needs.

So here is how

I knew right away that I had to limit the tools I used and cut corners where possible to fit into the tight budget of spending just a single month on this project.
I had to concentrate on the most necessary and yet powerful features only and make use of well established supporting services in order to develop this in such a short amount of time.
Luckily picking essential features was an easy call as I have worked extensively with all mayor email marketing apps and their APIs building integrations to my products over the past few years.
This way I knew exactly what I wanted out of an email marketing app.
I had as well already a huge list with little and bigger annoyances of existing solutions that I could improve on right away.
Sending the weekly newsletters was actually the simplest thing we could have used even an existing open source solution for. (Developed by freecodecamp which found themselves in a similar situation)
What was way harder to do and incredible more important to us was the ability for extensive personalization features through all parts of the App.
For example building visual workflows which use dynamically passed in variables at subscribers optin time, sends different type of emails and does different types of actions at different times in a personalized way for each subscriber:
(This is an actual screenshot from the finished App – Its a single workflow we use for double optins and we previously had 100 almost identical copies off just to handle different tags and downloads)
Luckily technology wise I could start from a blank slate.
The only requirements were the list of features I had in mind, but I wasn’t tied to any legacy code or specific platform.
This meant I could develop everything with state of the art technology like server-less cloud functions or an API first design.
It would scale as needed without the need of providing extra infrastucture or running into typical scaling issues, no matter if the App only had a single big customer (ourselves), or hundreds or even thousands of active customers of varying sizes.

The best possible time to be an indiehacker

What I found curious about this whole project is that just a few years ago you would have needed tens of thousands of dollars to invest, a small team and a very looong time to build something remotely similar.
Nowadays I was able to pull this off all by myself within just 4 weeks.
This is partly thanks to the amazing work of other people who came before us and advanced software and services which weren’t available just a few years ago.
There has never been a better time for any indiehacker to build and launch their own projects.
This is really the spirit of all of it: We are able to pull things off that can compete with well funded, big teamed startups.
So for what its worth here is the complete Tech Stack I used:

Firebase Firestore

I knew first hand that I didn’t want to handle running a highly available database with hundreds of thousands or even millions of rows of data added per month that gets written to in a very high burst fashion.
Firebase Firestore is exactly made to handle this completely hands off for you.
Beside that it offers some of the best developer experience I have ever seen.
They got SDKs for every major platform.
Everything is throughly documented.
The community is big and helpful.
The backend UI is state of the art and usable to edit things you might not have build an UI yourself for yet.
And one of the greatest things is that it integrates almost seamlessly with other google cloud platform services which came in very handy developing other parts of the App. (You will see what I mean further down this article)
It’s pricing is exactly what I was looking for:
It scales indefinitely with you but is basically free to get started with.
It supports a wide range of different needs, from this App I’ve been building or even communities like indie hacker (which runs on top of it as well).
There are certainly other (both more open or expensive) options if you don’t want to handle database servers yourself.
But I just love Firebase.
It certainly has some mayor down sides you should familiarize yourself with before going all in, but once you know what you are up against you can easily navigate around them. (Let me know in the comments if you would like a more extensive post about the pros and cons of Firebase)

UI Framework

Within just 4 weeks there was no way that I was going to design and write my own UI. So I wanted to give a mayor UI framework a try instead.
It has been years since the last time I really worked with any of the available ones, and even then the only true experience I had with was Zurb Foundation – mainly for their JavaScript parts and CSS grid classes in pre-flexbox times.
As I was more looking for ready made UI components I could just plugin and play I decided go for Bootstrap which turned out as a very good choice:
Almost all of the visible part of the UI was made out of the ready made components Bootstrap provides, with a couple of small adjustments here and there.
I gave googles material UI a quick try as well but found it way too difficult to get started with.
There is definitely a higher learning curve with Material Design.
Especially as it seems to be more tightly coupled with the JavaScript framework you use and particular focused on mayor frontend frameworks like Angular. (Something I didn’t want to work with and will tell you why in just a second)
Bootstrap was quicker and easier to start with and turned out truly excellent.
All in all I only wrote about 500 lines of CSS myself for this project which is probably a record as I can’t remember anything I’ve worked on in the past few years that had so little CSS. (Of course not factoring in the CSS provided by Bootstrap itself).

Javascript Frontend Framework

Nowadays the default choice seems to be React or Angular and people can get pretty religious about it.
While I certainly got some experience working with React I’ve become increasingly vary of its usage.
It just feels to me that what might be a great choice for a billion dollar company is not necessarily a great choice for indie hackers.
One thing that happened to me before is that React and its entire Eco System is just moving way too fast. You don’t touch a project for a couple of months and then when you try to update its dependencies you need days just in fixing breaking changes.
I wanted to make sure that the underlying framework I used for this was still relevant and easy to use two to three years from now as I was unsure how frequent I will do updates.
Its a very small framework based on pure web standards that is incredible fast and performant.
If you have used any other modern javascript framework before you will probably find it really easy to use as well.
Its simplicity makes sure that it won’t be out of date a few months down the line, and beside that it encourages you to not bloat your code.
Instead of installing yet another dependency for anything you are more likely to develop smaller, simple stuff you actually understand yourself using just vanilla javascript.
And if you are not used to writing plain javascript from scratch, than you can find help within one of the excellent JS communities like Go Make Things from Chris Ferdinandi. (Who actually helped contributing a small method to this project)
The App only had around 6 to 7 different views I had to build which a lot of repeating components and it was really easy to do this with HyperHTML in combination with Bootstrap and some utility libraries like momentjs.
Using HyperHTML was mainly my choice because I know it really well by now and have come to absolutely love it.
In the end it doesn’t really matter what you use to build you app and instead of worrying about that, you should just choose whatever you feel most comfortable with.

API Design

I’ve never designed a Rest API for myself but have extensive experience working with all kind of different APIs.
Therefore I thought that it shouldn’t be too had to build some simple API endpoints together with Node.js and Express.
Well… unfortunately that wasn’t entirely true.
I seriously tried building a Rest API but with my lack of experience in the area it just wasn’t feasible to get it done properly within such a short amount of time.
Building a proper Rest API turns to be out way harder than you might think. (Which is probably the reason why there are so many low quality APIs out there…)
Fortunately I wasn’t really forced to build a rest API but could use whatever worked best for me in this particular situation.
And for this project using GraphQL turned out to be the better choice.
It definitely has a learning curve but if you have never designed an API before its probably way faster than building your own Rest Endpoints from scratch.
Beside that its declarative approach encourages a lots of best practices you would need to make sure to think of yourself when building a REST API instead.
I know that I am might contradicting my thoughts about the JS Framework going with an almost “overhyped” choice backed by a huge company. But if you are doing something for the first time the size of the community actually matters as there are excellent resources available to learn from and ask questions.

Serverless Backend

For the API and as well all Cron Jobs I used Google Cloud Functions / Firebase Functions.
The reason is simple:
They allowed me to easily trigger things whenever something in the Firebase Database happened.
And they would scale indefinitely with the needs of the App without me ever having to worry about the servers that run the whole thing.
Using the same language (JavaScript) across the whole application stack makes your life a lot easier as well. Coming from a PHP Background I nowadays actually prefer JavaScript above all else as I can share code between the Frontend and Backend whereever I see fit.
Pricing wise you got to be really careful with the things you put within your functions as economies of scale can really hurt you here.
Simple example:
In the beginning I had a couple of functions that took 20s of CPU time at peak hours (because it was waiting for a response from another Rest API). Being executed millions of times this gets VERY expensive VERY fast because you are mainly billed for the CPU time you use.
Optimizing just a single function to take 500 miliseconds instead 20 seconds makes a 4000% difference across millions of executions.

Aggregations and Segmentations

The bad thing about building an email marketing app is that one of the most important parts of your application is actually running aggregations and segmenting your data.
And unfortunately this is exactly the thing Firebase Firestore (or any document based database for that matter) is not great in.
It really depends on what you are building if the aggregations and segmentation features of your database are sufficient, but for this App I unfortunately had to add something especially made for that to the Mix:

Elasticsearch

Elasticearch is basically a search engine for your database that is incredible fast in both searching, indexing and aggregating.
Unfortunately its as well the only part of the application that needs to run on servers. (I am using a couple of powerful Digitalocean VPS for this)
Even if I initially didn’t want to touch any servers for this app, the reason I still decided to do this is because its one of the most incredible and robust pieces of software I have ever come across.
Building a highly available cluster of servers is incredible easy (if you have at least some experience setting up and running servers on your own) and once its up its just mind blowing how smoothly it runs.
I have yet to find something I can’t do with Elasticsearch. (And I found many things I couldn’t do easily with Firebase)
It is a mature software with a huge community and throughly documentation which is just plain fun to work with.

Email Sending

I have worked with another transactional email provider before called Mandrill (made by MailChimp) – unfortunately since a while now its for MailChimp Customers only.
So if you want to send a LOT of emails you basically are left with two choices:
Amazon SES or Sendgrid.
And while Sendgrid looks and feels like the sexier option, its actually as well a lot more expensive if you don’t reach HUGE volumes that might warrant a special deal.
So we went with Amazon SES as it has essentially the same features and is pretty affordable right away.
Building on top of it was mostly smooth. It takes some time to set up properly but then you get real time notifications for sending, delivery, bounces, opens and click rates build right in – some information we weren’t even able to get before.
The sending limits are pretty generous and we yet have to reach the daily quota:
As a matter of fact it was quite a challenge to actually reach our maximum send rate of 200 emails per second. (Which is 12.000 per minute or an email to 100.000 subscribers within roughly 8 minutes).
Which brings me to the last point:

Challanges I didn’t think of

All in all building this went fairly smooth once I figured out what was and wasn’t possible with Firebase.
We transitioned quite abrupt on beginning of Feburary to the new system without any mayor bugs or downtimes.
The biggest difficulty in building this was actually handling such a high amount of data produced in relatively short amount of time.
As a simple example imagine that you send an email to 70.000 subscribers.
For each subscriber you have to personalize each email. Not just the delivery name and address but as well the content and links within the email.
Then each sended email produces quite a few number of “notifications” for sending, delivery, rejections, bounces, opens, clicks and so forth. Each of those notifications needs to be handled and acted upon, changing a single piece of data within your database or triggering complicated, stacked automations in other parts of the App.
So a single “broadcasts” produces roughly 300.000 new pieces of information you need to handle within just a few minutes. Then there are of course other times where almost no emails are sended and not much happens:
Sending Emails based on the subscribers time zone helps mitigating this (so that not all emails are sended at exactly the same time).
Pipelining your events through something like Logstash and handling them at a slightly slower pace your database can easily handle is another solution.
An additional challenge was making sure that emails are not sended twice to the same subscriber. With multiple independent workers sending a lot of emails in a short amount of time this was easier said then done.
UI Wise the most difficult task was building the visual workflows. They have various degrees of recursion and a lot of edge cases that were hard to build and debug. Recursion is definitely a topic hard to wrap your head around and even harder to master.

Conclusion

I wrote this post because a lot of times while building this I would have loved the possibility to reach out to somebody who has done something similar before and ask them questions.
I always imagine this fairy tale office world where you work in an amazing team and got experts you can turn to for everything.
Indiehackers on the other side have to be generalists. We usually work all by ourselves and I think sharing our experiences, difficulties and thought processes with each other in communities like this one can help tremendously.
Of course for the moment I only build and launched this as a tool we can use internally.
If I would have had to build signup flows, customer on-boarding features, billing processes, a marketing website and the millions of things I haven’t thought of it would have easily taken me double or triple the time.
I still think that it was one of the most interesting challenge I’ve ever done that trained me to build and launch something with both limited resources and time. This is exactly the situation most of us find themselves in all the time why trying to build and launch our side projects or businesses.
I hope you enjoyed the journey and let me know in the comments if there is a topic you would like to expand me upon.

By Jascha Brinkmann

Sourced from Hackernoon

By

The use of analytics is no longer limited to big companies with deep pockets. It’s now widespread, with 59% of enterprises using analytics in some capacity. And companies are capitalizing on this technology in several ways. For example, at our agency, we typically scrub big data for advertising insights for our clients. And many of the companies we’ve worked with revolve their entire market strategy around the insights pulled from new data.

According to a survey from Deloitte, 49% of respondents say that analytics helps them make better decisions, 16% say that it better enables key strategic initiatives, and 10% say it helps them improve relationships with both customers and business partners. But in order to take full advantage, you need to know how to get the most value from your data.

Data Quality Standards

There is certainly not a lack of data available. However, the quality of that data still leaves much to be desired. A study from the Harvard Business Review discovered that data quality is far worse than most companies realize, saying that a mere 3% of the data quality scores in the study were rated as “acceptable.”

This is problematic because low-quality data adversely impacts many areas of business performance. In particular, it can translate into incomplete customer or prospect data, wasted marketing and communications efforts, increased spending and, overall, worse decision-making. Therefore, improving data quality should be a top priority for all businesses.

There are a few ways to go about this but, in my opinion, as an agency owner, one of the best approaches is web data integration (WDI). WDI is a process that aggregates and normalizes data and presents visuals and other reporting that makes analysis easily digestible. WDI relies on a similar premise as web scraping but is far more comprehensive. It also has the ability to make data “intuitive” — something that’s essential for capitalizing on the massive volume of data that’s out there.

It allows you to take a large volume of data from a myriad of sources and break it down in a way that makes client analysis much easier to do. For us, if we’re looking to clean up data quality, this process helps us present data back to clients in a cleaner fashion.

Before formally choosing to implement WDI, businesses should first determine what specific goals they have for data sets and then decide whether an in-house solution or a managed service through a third-party provider is the better option.

Another way companies are fully leveraging data is through machine learning, where computer systems learn, improve and evolve as they take in new data.

Assessing Data Quality

So, how can you tell if you’re dealing with low-quality data? In a Harvard Business Review article, data experts Tadhg Nagle, Thomas C. Redman and David Sammon recommend the following key steps:

Gather a list of the last 100 data records you used or created.

Then, focus on 10-15 key data elements that are most integral to your business operations.

Have management and their teams go through each data record and identify any noticeable errors. Examine the results. (In my opinion, an easy way to go about this is to create a spreadsheet with two columns — one for perfect records and another for records with errors.)

Once you look through the results, the quality level of your data should become obvious. If more than two-thirds of your records have errors, that’s usually a sign that data quality is hurting your performance and needs improvement.

Here are a few other data management tips:

Move all of your data to a centralized database to create a standardized data architecture.

Ensure your employees are up to date on all aspects of data best practices, including data entry, management, compliance and safety.

Create data management hierarchies if you have multiple teams to keep it all organized and reduce the odds of a breach occurring.

Designate certain team members to handle core data management.

Choosing The Right Tools

Data is one of the most valuable assets a business can have and potentially has a tremendous impact on its long-term success. That’s why it’s vital to utilize the right tools and technologies to fully leverage all available data and make it as accurate as possible.

Here are some specific things we look for when assessing tools/technologies for accurate data analysis:

Data normalization for simple organization

Shareable dashboards for streamlined communication between team members

Fully mobile

Third-party integration

When searching for tools, it’s wise to request a demo of any platform you’re considering to get a hands-on feel of how it works, what the dashboard is like, how intuitive it is and so on. Do you naturally like the look and feel of the product right off the bat? Or do you find the experience to be friction-filled? First impressions are everything, so you want to ensure the product feels right to you.

Final Thoughts

Analytics has come a long way in a relatively short period of time. It can aid in multiple aspects of operations and be a real game-changer for many businesses. But to get maximum results, companies need to know how to properly utilize this technology, improve the quality of their data, and effectively manage it. Those who are able to do so will have a considerable advantage over the competition, and be poised to succeed in 2019 and beyond.

Feature Image Credit: Getty

By

Partner at K&J Growth, serving some of the world’s largest companies through smarter marketing | Partner at Rugby Bricks.Read Kale Panoho’s full executive profile here.

Sourced from Forbes

By E.J. Samson.

A new study shows just how much consumers want brands and culture to mix

Commerce and culture have always intersected—even though it can be a fine line for brands to walk. But what surprised the research team behind MAGNA and Twitter’s new study, “The Impact of Culture,” was just how much consumers—particularly younger people on Twitter—expect and even want brands to be culturally relevant: aligning well with cultural events, promoting trends that define today’s culture and supporting social issues that benefit everyone.

Insight-rich results

Brand involvement in culture is especially important among consumers between the ages of 18 and 35, and those on Twitter versus the general population are more passionate, informed and feel more strongly about brands aligning with culture.

The study found that brands can become more relevant by embracing culture by staying current, demonstrating knowledge of consumers and giving back. When people are deciding which products and services to buy, they’re not only thinking of basics like price and quality—or even more amorphous concepts like reputation. They are also assessing just how much a brand reflects their interests and supports the issues they hold close to their hearts.

Incredibly, a brand’s cultural involvement makes up a full 25 percent of a consumer’s purchase decision. That means being involved in culture is a significant consideration when people are weighing whether or not to buy something, alongside other factors like positive brand perception, price and quality. It’s a finding that should make marketers rethink their focus and strategies, since cultural relevance can be established with one campaign, whereas other factors are relatively more intractable.

twitter1

While jumping on trends and cultural happenings in realms like sports and music are table stakes for brands, the study reveals that people want to go even deeper: Americans might love their reality TV, but survey respondents say they are more informed on issues like gender equality and fair trade than pop culture events.

What does this mean for marketers?

Go where the most leaned-in and influential people are already gathered: A key revelation of the study is that while culturally passionate consumers tend to be younger, what really sets them apart are their media habits. Social media usage is a 25 percent stronger indicator of cultural passion than age. According to our study, culture-focused ads work harder on Twitter than on other premium sites, where audiences of true tastemakers are most engaged and most receptive.

Live out the values of your customers: While there are many ways for a brand to be involved in culture, according to survey respondents, the top ways include giving back to the community, putting customers first, being inclusive of a wide audience and supporting social issues that benefit everyone.

Have a strong POV in your ads: Culture-focused ads succeed in positioning brands as relevant. They also position them as socially responsible and innovative. And they create a more memorable experience for consumers.

This new research makes a strong case for brands to acknowledge, and even actively improve, the culture that permeates all of our lives so fully. And expressing their engagement with culture on platforms like Twitter is the best way for brands to join the liveliest conversations of the day.

By E.J. Samson.

E.J. Samson is the lead content strategy manager for Twitter’s Global Business Marketing team. Follow him on Twitter @ejsamson.

Sourced from AdAge

Channel 4 and Virgin Media are adopting Sky’s AdSmart advertising system. Sky says it can put viewers into groups of 5,000 or more based on age, location, lifestyle, and “even if they have a cat”

Personalised advertising already stalks us across the web, and it’s coming to our TVs, with Channel 4 the latest broadcaster signing up to use Sky’s AdSmart to target commercials. While such a system isn’t quite as invasively personalised as the behavioural advertising clogging up the internet in order to show us shoes we’ve already bought, it could have a big impact on television – and risks being rather creepy.

AdSmart is Sky’s system for targeted, addressable ads, which are commercials that can be swapped out and personalised based on location or other personal data – even in live-broadcast, linear TV. Sky has used the platform on its own channels since 2014, and has this year signed up Virgin Media and Channel 4 to do the same.

For viewers, the benefit is not being shown irrelevant ads – Sky won’t show you ads for its broadband if you’re already a customer, for example – and Sky points to research that suggests there’s a 48 per cent drop in channel switching when such targeted ads are shown. For businesses, small companies can target a specific, hyperlocal catchment area rather than throw away money on nationally shown commercials, opening up TV advertising to smaller companies.

And for broadcasters, the benefit is they can charge more, perhaps as much as ten times more, for what they say are more effective ads – helping to claw in more cash as advertising revenues stall. “Better targeting can be beneficial for both advertisers and viewers: it can not only increase ad return on investment for advertisers, but also deliver more relevant information to viewers,” says Yiting Deng, assistant professor of marketing at UCL. Richard Broughton, researcher director at Ampere Analysis, suggests by a rough estimate it could bump revenue at Sky by as much as 10 per cent and across the wider industry by 2 per cent – it’s positive for broadcasters, but its financial impact is limited.

No wonder then that targeted television ads are already in use with on-demand services; Channel 4 earlier this year rolled out a tool letting brands use their own data to match ads to audiences. But swapping out ads is a bit more difficult with live television. “The key technology is combining what is called addressable advertising, which is personalised, with programmatic systems, which is enabling the purchasing of ads automatically,” says James Blake, director of the Centre for Media and Culture at Edinburgh Napier University.

According to Sky, AdSmart turns your set-top box into a local ad server, downloading and storing commercials deemed relevant based on the data the company holds on you. When watching an AdSmart-enabled channel, those ads will be swapped into the commercial break spot; if there are no AdSmart ads available – or you’ve opted out – a generic commercial is shown instead.

To do this, AdSmart and broadcasters that use it require data about viewers. That could be limited, as a local small business could target a handful of postcodes, with a different ad shown to everyone else, with no personal information required. Sky says that location is a key attribute, though there are thousands more, noting that Huddersfield Town Football Club advertises season tickets locally; there’s not much point in showing that commercial to football fans in Scotland, after all. Location can also be used to target ads more carefully using demographic information; if a neighbourhood is more likely to have family homes, showing ads targeting parents makes more sense.

But targeting those ads more precisely – such as showing pet food ads only to those with cats and dogs – requires more data, which broadcasters purchase from third-party data brokers. Sky, for example, says it can select viewers in groups of 5,000 or more based on age, location, lifestyle, and “even if they have a cat”, using Sky’s own customer data, information provided by the company wishing to advertise, and data bought in from third-party brokers such as Experian, Dunnhumby, CACI, 20ci, Mastercard, Emma’s Diary, and Game. Companies such as those have already been targeted with GDPR complaints for exploiting our personal data and selling it on to marketing companies. If you want to know what data Sky et al have gathered on your family, you can file a subject access request.

Technically, it’s possible to make addressable ads more tightly personalised than those groups of 5,000 used by AdSmart, but there’s a danger that could put viewers off, notes Blake. “I think TV companies and broadcasters need to be careful how they use personalised advertising,” he says. “There’s a risk these adverts can be creepy.” Blake points to an experiment in 2017 when viewers on the Channel 4 app were shown adverts with their own names, which some people found “a little bit creepy”, he says.

There’s another reason TV commercials aren’t likely to get quite as personal as online ads: they cost more to make. “You’ve got additional costs for producing high quality TV adverts – the creative process in itself is quite expensive,” Broughton says. “So this is about refining your spend, as opposed to micro targeting a specific segment.”

While there’s merit in avoiding ads for products you’d never buy, such targeted ads could also be used for political marketing – and that raises concerns for democracy when we’re not all seeing the same message, though Blake notes that broadcast television advertising in the UK is heavily regulated. “That’s one of the big reasons why TV is trusted in the way it is,” he says. “But we need to be aware of the risks because TV adverts can be hugely powerful and we don’t want political campaigns and parties to misuse that. There is a danger that you end up in a bubble of like-minded people with like-minded messages, and don’t get exposed to sentiments on the other side.” However, in the UK, such commercials are banned, with unpaid allocated spots given to the parties instead.

And that’s another reason TV ads aren’t likely to be as invasive as online counterparts: they’re heavily regulated. Broadcasters face tighter regulation than online advertisers, and GDPR should limit how personal data is repurposed for marketing. “Addressable advertising in TV took a hit when GDPR came on board,” says Blake. “Before GDPR, there was a lot of discussion about how cookie data [from web browsing] could feed into adverts. And I think GDPR made that process take quite a big hit.”

Both Sky and Channel 4 say they follow GDPR’s rules, and both allow viewers to opt-out of AdSmart, with Sky adding that any “special category data”, such as information about your health, needs consent to be processed by AdSmart.

If such ads do come off as creepy, you can opt out – and not only of AdSmart, but the broadcasters themselves, something they’ll be wary of. As Broughton notes, angering customers doesn’t have much value to broadcasters such as Sky that can cost up to £70 a month. “It’s not worth jeopardising that to get a few extra pence out of an advertiser,” he says, predicting that “they’ll err on the side of caution.”

Feature Image Credit: Getty Images / WIRED

Sourced from WIRED

We know that exceptional content is what makes a brand. We also know that analysing our data to very specifically target audiences is crucial for great ROI. But we rarely put the two together and use the data available to actually analyse what content works – and why.

Yet knowing exactly why content works can give us that winning edge. And, luckily, the ability to see what indisputably resonates the most with our audience – and drives our bottom-line – is already in our hands.

The state of play

In the climate of the current ‘data boom’, audience targeting naturally takes precedence, with the majority (55%) of marketers saying ‘better use of data’ for audience targeting is their priority in 2019, according to Econsultancy.

It makes sense. On a daily basis, we’re faced with countless blogs, podcasts, speakers and everything in-between promising that if we perfectly optimise our targeting, our messaging will beat the daunting odds of the 0.9% CTR cited by WordStream. And so, we dedicate hours and hours every week to creating personas, hypothesising about audiences, segmenting users and running lengthy A/B tests to find the piece of content that our audience love. We add to our already-complex marketing stacks tools that tell us what messaging has been more successful, in order for us to optimise.

But when we do find that winner, do we know why it works? Do we know exactly what features caused the higher CTR? Do we know how we’re going to recreate it in our next campaign, to make it better, even?

This lack of knowledge – despite all the tools and techniques we use to offer insight – is what we at Datasine call the ‘black box’ because when it comes to understanding why, we are left in the dark. Just looking at results doesn’t give us the insight needed to truly understand content preferences in an actionable way.

Semantic content analysis

To crack open the black box, we need to start conducting in-depth semantic analysis of our content. Only then can we begin to truly understand why some content resonates and some doesn’t.

As experienced marketers, we come prepackaged with a deep understanding of – and fascination with – psychology and our audience, meaning we’ve already got the skills on paper to analyse our content. It’s simply a matter of breaking it down into parts. We’ll look at this in terms of images and text.

If you want to analyse your imagery, you can take all the image assets you’ve ever created and note down the particular elements you used in each, then check to see if there are any patterns which relate those choices to your ad performance.

For example:

  • Did you use a photo of your product outdoors? Or in the showroom?
  • Were people visible in the shot?
  • What was the size of the text, and the colour of any overlays or CTAs?

It may even be worth inviting a panel to judge your images on the emotions that they evoke, or photographers to assess the quality and composition of the shot.

You can do the same for text content, approaching this by categorising how you describe your product or service. For example:

  • Do you appeal to your product’s ease of use?
  • Are you emphasising your innovative credentials?
  • Do you use particularly casual – or formal – language?

With this process, we can see which types of content are receiving the most engagement. And we can use these features to keep creating great campaigns that we further optimise as our understanding of customer content preferences grows.

Scaling content analysis

If we have just a few campaigns on the go, content analysis is easier, but it gets harder as we scale. It stops being practical to expect humans to spend days, weeks, even months labelling what goes into each piece of content. Here’s where machine learning and artificial intelligence (AI) come to the rescue.

AI models can extract all of these elements in seconds by analysing image or text semantically to look at content like humans do. That way, we can cut back on lengthy, expensive A/B testing, and get rid of guesswork once and for all – a vision we at Datasine are working toward. Our AI platform Connect (formerly Pomegranate) automatically identifies the most effective content for your audience.

By embracing semantic content analysis and working collaboratively with AI, we can feel confident in understanding exactly what content is going to work before we hit send.

Sourced from The Drum

By

Developing a holistic data strategy

Enterprises of all sizes, all over the world, have now recognized that data is an integral part of their business that cannot be ignored. While each enterprise may be at a different stage of their personal data journey – be it reducing operational costs or pursuing more sophisticated end goals, such as enhancing the customer experience – there is simply no turning back from this path.

In fact, businesses are at the stage where data has the power to define and drive their organisations overall strategy. The findings from a recent study by Infosys revealed that more than eighty-five percent of organisations globally have an enterprise-wide data analytics strategy already in place.

This high percentage is not surprising. However, the story does not end with just having a strategy. There are numerous other angles that enterprises must consider and act on before we can deem a data journey as successful.

Developing a data strategy

First, enterprises need a calculated strategy which covers multiple facets. Second, the real life implementation of the strategy must be seamlessly carried out – and this is where the challenge lies for all enterprises.

Consider having to create a comprehensive and effective strategy for your company. Data strategy is no longer about simply identifying key metrics and KPIs, developing management roles or creating operational reports, or working on technology upgrades. Rather, its reach extends to pretty much all corners of the business.

In short, data strategy is so tightly integrated with business today, that it is in the driver’s seat, which is a momentous shift from more traditional approaches of the past.

What are the characteristics of a good, strong data strategy?

Creating a good, strong data strategy begins with ensuring complete alignment with the organisation strategy. The data strategy must be closely aligned to the organisational goal, be it around driving growth or increasing profitability or managing risk or transforming business models.

Not only that, but the data strategy must be nimble and flexible, allowing periodic reviews and updates to keep pace with wider changes in the business and market. The data strategy should be able to drive innovation, creating a faster, better and more scalable approach.

A strong data strategy must be built in a bi-directional manner so that it can enable tracking of current performance using business intelligence to provide helpful pointers for the future. This approach is only possible if organisations choose to adopt a multi-pronged data strategy that encompasses people, technology, governance, security and compliance. Importantly, organisations must also choose to adopt an appropriate operating model.

Taking a holistic approach to data

A holistic approach includes developing a defined vision, having a clear structure around the team and factoring in the current skill set of the team. This is in addition to considering what the enterprise can reasonably anticipate in the future and identifying mechanisms to successfully drive the change across the organisation.

The technology component involves having a distinct vision, assessing the existing solution landscape, all the while being cognizant of the latest technological trends and arriving at a path that fits well with overall organisational goals and the technology vision.

Governance, security, and compliance are other critical aspects of a good data strategy. Integrity, hygiene and ownership of data, plus relevant analytics on the data to determine the Return On Investment on data strategy, are all essential steps which cannot be forgotten. We cannot overstate the importance of security.

Adherence to compliance has assumed significance with various regulations in play all over the world, such as GDPR in Europe and new data privacy laws in California and Brazil for example.

In essence, the data strategy must define a value framework and have a reliable mechanism to track the returns to justify the investments made. About fifty percent of respondents to our survey agreed that having a clear strategy chalked out in advance is essential to ensuring an execution that is effective in practice and goes off without any hiccups.

Identifying the best strategy is essentially pointless if the execution falters

Many obstacles have the power to prevent the flawless execution of a data strategy. Copious challenges in the technology arena can arise in various forms, for example: having the knowledge to choose the right analytics tools, lack of availability of people with the right skill set, upskilling, reskilling and training the workforce with the necessary skills for the world of tomorrow and so on. Most of the challenges articulated by respondents to the Infosys survey arose in the execution phase of a data strategy.

While these challenges may appear daunting in the first instance, they can be addressed with careful planning and preparation. Being prepared and equipped for multiple geographies, multiple locations, multiple vendors, talent acquisition and good quality training are just some of the numerous possible ways companies can begin working towards smooth execution of their digital strategy.

Feature Image Credit: Image credit: Pixabay

By

Gaurav Bhandari, AVP and Head of Data & Analytics Consulting at Infosys.

Sourced from techradar.pro

By Teradata and the Forbes Insights team

In a world overflowing with siloed petabytes of data, understanding can come only with data analytics that are seamless and all-pervasive.

Having access to all the data at every point in time leads to insights and drives powerful outcomes that make the difference between good and great.

Imagine a patient being rushed to the ER after an accident or a cardiac arrest and not being able to get a critical test with diagnostic equipment in these split-second, life-and-death scenarios. Maximizing availability, or uptime, of equipment, therefore, assumes top priority.

Healthcare 4.0 is all about gathering relevant and valuable data and using it in applications that translate into improved healthcare management, efficiency and cost-control. In particular, using data analytics to bolster predictive maintenance of equipment can help ease the workload of healthcare service providers so that they can spend more time focused on their patients.

The Scenario:

Siemens Healthineers is a global medical equipment manufacturing company with a presence in more than 70 countries. There is an installed base of about 600,000 active systems around the world, allowing for roughly 240,000 patient touch points every hour.

Many of the systems are remotely connected to Siemens Healthineers, e.g. for the purpose of predictive and reactive maintenance. The logistics of managing the significant amount of machine data generated is staggering.

The Challenge:

Modern medical equipment is typically highly complex with a lot of specialized parts. Healthcare providers invest in such equipment in order to provide excellent care to their patients. When the equipment fails, it not only inconveniences patients who need to reschedule their tests, it also proves to be challenging for healthcare providers who want to provide the best possible care.

Access to real-time data processing becomes absolutely vital to increase uptime of the equipment, operational efficiency, productivity and cost-effectiveness, and to decrease service parts usage, shipping and inventory. It’s a complex challenge to connect people, machines and data in a heavily regulated industry and rapidly moving IoT trends. And all of that in compliance with industry-specific and general data privacy laws, such as the EU General Data Protection Regulation.

 

Siemens Healthineers leverages Teradata to aggregate technical data from devices, both machine and process data, along with service parts and labor efforts to enable better outcomes at lower costs for customers.

“By transforming our data assets into concrete actions for our global service organization, we reduce the extent of technical expertise needed to maintain a system,” says Stefan Meiler, head of data governance and analytical services at Siemens Healthineers. “We avoid first-level onsite troubleshooting, optimize the service delivery process and minimize productivity losses from downtime situations for our customers.”

 

Faced with the challenge of diverse datasets siloed into machine data, service data, sales data and customer-related data, Teradata helps Siemens Healthineers put it all together in one data analytics software platform, Teradata Vantage. Once all the data is combined, Teradata connects the dots so that it can be used effectively for analytics by creating the bigger picture.

“Transforming data into value requires multiple development iterations while leveraging innovative, scalable and easy-to-use technologies. Our data stewards are transforming a wide variety of data assets into curated, single point-of-truth datasets,” says Dr. Mirko Appel, head of analytical services at Siemens Healthineers. “On top of these datasets, our data analysts develop dashboards to create transparency on our global installed base and service operations, while our data scientists are leveraging this transparency to automate data-driven services by applying artificial intelligence. Teradata Vantage enables us to accelerate all of these iterations while providing a fast, reliable and scalable backbone within our global organization.”   

 

About 15 years ago, Siemens Healthineers invested in a secure remote services infrastructure that enables a comprehensive understanding of the state of their products — how, where and when common part failures occurred.

This not only minimizes equipment downtime but also provides substantial savings by avoiding sending technicians out to make repairs that were not needed in the first place.

This move from a reactive to a predictive model that anticipates service needs in advance has successfully translated to a higher first-time fix rate. It has also created opportunities for new types of service contracts with their customers.

 

Siemens Healthineers works with Teradata to create smart data assets to aggregate and consolidate the data in a ready-to-use form for its data analysts and data scientists so they can focus on discovering insights from the data. Using predictive maintenance for superior asset utilization has optimized equipment uptime. The increased operational efficiency has helped them provide answers to their customers even before they had thought of the potential issue, leading to high levels of customer confidence and satisfaction.

“The application of pattern recognition algorithms on machine data has transformed our service delivery towards a proactive approach: We are now able to identify certain system failures up to 21 days before they have an impact on the machine’s performance,” says Torben Scaffidi, head of lab operation analytics at Siemens Healthineers. “By this, we can increase the efficiency of our service delivery significantly and our customers are pleased with more valuable uptime to deliver care to patients. Win-Win for everyone: Siemens Healthineers, our customers and the patients.”

 

The amount of data that will be generated today and tomorrow will continue to grow exponentially. What will the next use case be? What will the next data format be? How to automate processes through machine learning for flexible solutions as the business expands? For answers to broader issues like these, Siemens Healthineers needs a global partner like Teradata to grow with.

                                                            

Heavy regulations surrounding the healthcare industry as well as stringent data privacy laws add to the challenges of providing quality healthcare. Keeping track of the rapid changes in trends outside the healthcare industry, especially when it comes to the Internet of Things, is extremely challenging. Teradata operates in a wide range of industries and this also helps Siemens Healthineers benchmark themselves not only against the best companies in their sector but the best companies across all industries

 

As a company that is constantly innovating, Siemens Healthineers has a vast array of new and emerging R&D initiatives. With all these great new products comes the challenge of keeping track of the data coming from these new technologies. These may deliver new data structures and new predictive models. Learning from previous products and designing new data structures into new systems becomes crucial to data analytics in the future.

Answers must lead to better outcomes. Siemens Healthineers is looking at the next level of customer engagement by engaging online with customers interactively. The answers that they have through their data will also be at the fingertips of their customers so they can have a better understanding of their products and provide better outcomes for their patients.

 

At the heart of data analytics is the ability to transform vast amounts of data into actionable insights that organizations around the world can use to drive powerful outcomes. Better-informed decisions generate confidence, improve efficiency, nurture creativity and empower people to think of unique ways to push the envelope for social good.

By Teradata and the Forbes Insights team

Sourced from Forbes

Outset Agency, Basement 7 Pembroke Street Upper

Outset Agency seeks a highly motivated, creative and skilled Senior Account Executive to join our growing team, who will have a particular focus on the Creative Campaigns area of our business.

We are a new-age agency that focuses on how audiences and businesses connect. We work directly with both brands and agencies, helping them to achieve their goals.

Our work is focused across 3 key areas; Creative Campaigns, Influencer Marketing & Live Experiences.

The successful candidate will have experience working within the client services area of a marketing or media agency and have a strong knowledge of all the different marketing functions across digital, content, experiential and beyond.

The Senior Account Executive will be responsible for managing a portfolio of client accounts, as well as assisting the Key Account Manager on other projects.

Responsibilities

Responsibilities:

  • Liaising with clients on a daily basis – with the Client Services team the lead point of contact on all Agency work
  • Plan, develop and assist in the delivery of client campaigns and projects
  • Plan and manage client budgets and advise on campaign elements and distribution channels
  • Internal project management of Agency’s work across Digital, Content, Influencers and Events
  • Supplier management and building business relationships
  • Creation of client proposals
  • Working closely with and reporting into the Key Account Manager on all Agency work

Requirements

Requirements:

  • Minimum 3 years of experience working in Marketing or a related field (preferably Agency side)
  • Strong understanding of current online and offline marketing concepts, strategies and best practices
  • Ability to prioritise tasks and focus on multiple projects and deadlines simultaneously
  • Strong presentation skills
  • Excellent communication skills with the ability to interact professionally with clients and business stakeholders
  • A creative thinker – we want someone that is resourceful and has the ability to think outside the box
  • Ambition – we are young company that is moving and growing quickly, there is a huge opportunity for personal growth within the business for the right candidate

Click HERE to apply for this job.

Brandface, 47 Terenure Road E, Dublin

InSight Marketing is one of Ireland’s top independent Marketing Agencies, specialising in Shopper, Promotional, Digital & Live Marketing we work with the best brands in the country and in the world, creating hard working marketing campaigns that deliver an action and a reaction.

One of our Client Service Teams is expanding to meet the needs of our growing client base and we are looking for a brilliant Account Executive to join the team immediately.

You are either an Agency experienced Account Executive who is ready to fast track their career working on great brands, in a great team OR a top notch graduate who has already done an Internship in a Marketing Agency and can demonstrate the right attitude and the right skills set for the job.

Either way, you need to love the world of Marketing Agency and thrive on the pressure that comes with delivering flawless Campaigns and Brand Experiences.

Sounds like you? Then we want you on our team! There is a great package available for you!

Requirements

  • Strong oral, written, and verbal communication skills
  • Exceptional administrative skills
  • Strong organizational skills with emphasis on time management
  • Ability to work independently
  • Strong attention to detail
  • Exceptional proficiency with Microsoft Excel

Click HERE to apply for this job.

Xanadu, Cork

Reporting into our Chief Marketing Officer, you will help shape Matchbook’s search offering, ensuring the team and agency is managed well and keeps up-to-date with all industry developments. You will work across a range of channels, leading Matchbook’s strategy across the full paid media mix.

To be considered for this role, you must have be operating at a senior level within paid media. You will have management experience and be able to operate both strategically and at a hands-on level. Ecommerce experience is essential.

This is an excellent opportunity for an experienced and senior paid search professional to really mould Matchbooks product and offering.

Ownership overview:

PPCAdwordsSEO

Paid Social VODLanding pages

App store page

What will I be doing?

  • Formulate and deliver the digital marketing & growth strategy to deliver on acquisition and retention targets.
  • Develop, implement and manage the company’s digital strategy across channels such as Pay Per Click and SEO.
  • Assist with the execution of Integrated Marketing campaigns to help raise awareness of Matchbook’s products.
  • Analyse and optimise the conversion effectiveness of digital platforms and campaigns.
  • Drive the implementation and adoption of key digital marketing tools (e.g. Analytics & Tracking applications) to continuously improve results and raise awareness of marketing / business KPIs.
  • Manage all aspects of relationships with third party vendors and digital agencies and ensure their efforts are aligned with the digital marketing strategy.
  • Act as the technical link between Marketing and Product teams, mapping out marketing technology capabilities and requirements in terms of in-house and third party systems, tools & technologies in an effort to boost acquisition, retention, and improve the betting experience of users.
  • Responsible for budget planning, forecasting, monitoring of spend and channel performance, and reporting of performance to the wider team.
  • Keep up to date with the latest digital marketing best practice.
  • Manage, train and develop a digital marketing team and ensure best practices are followed.

What are we looking for?

  • 3 to 5 years Digital marketing experience
  • Specialising in paid acquisition and retention across PPC, Search, and Social
  • Expert in search engine optimisation with proven track record of increasing organic search visibility and traffic
  • Analytical, numerate, a commercial focus, and strong attention to detail
  • Track record of building successful digital campaigns
  • Interest in sports and betting preferable
  • Ability to work collaboratively in a team environment as well as independently.

Xanadu is an equal opportunities employer

Click HERE to apply for this job.