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This is probably the most common question I got asked beside “How did you land your job in Data Science/ Data Analytics?” I will write another blog on my job hunting journey, so this will focus on how to get the industry exposure without that gig yet.

I gave a talk on this topic before at DIPD @ UCLAthe student organization dedicated to increasing diversity and inclusion in the fields of Product and Data that I co-founded. However, I aim to expand this topic and make it accessible to a broader audience.

And there it goes, I hope this post will potentially inspire more and more data enthusiasts to start their own blogs.

This may be a tough time for many of us, but it’s also a prime time to turbocharge and level up your skill sets in data science and analytics. If your employment got impacted at this time, treat the unfortunate as a great opportunity to take a break, reflect and kickstart your personal project — things that are luxurious when time does not allow.

“When one door closes, another opens” — Alexander Graham Bell

Hardship does not determine who you are, it’s your attitude and perseverance that define your values. Let’s get right into it!

Where to start?

Photo by Carl Heyerdahl via Unsplash

Start small and scale up

Before we start any project, first narrowing down your interests. This is your personal project so you will have full autonomy over it. Find something that makes you tick and gets you motivated to devote your time!

There will be a lot of challenges along the way that may discourage or sidetrack you from accomplishing the project, the thing that keeps you going should be the analysis topic that strongly aligns with your interest. It does not have to be something out of the world. Ask yourself what is important to you and why should we care about it.

When I first started, I knew that I wholeheartedly care about mental health and the ways to gain more mindfulness. So I dug deeper into analyzing the top 6 guided meditation apps to understand which one will be most suitable for my preferences.

Getting inspirations

Photo by Road Trip with Raj via Unsplash

Read, read, and read!

One of the most important key factors that I learned through my research assistant position at CRESST UCLA is to balance the workload between analysis and literature review. What this means is that we need to find what has been done in the past and figure out which additions or unique aspects you can contribute on top of the findings. My reading sources vary from Medium, Analytic Vidhya, statistics books to any relevant sources that I can find on the internet.

Take my Subtle Couple Traits analysis for example. There has been some work done in the space of music taste analysis via Spotify API, but no one has really delved into movies yet. So I took this chance and discovered the intersection of our couple’s cult favorites for music and movies.

Finding the right toolbox

Photo by Giang Nguyen via MinfulR on Medium

Now you get to this step where you need to figure out which data to collect and find the right tools for the job. This part has always resonated intrinsically with my industry experience as a data analyst. It’s the most challenging and time-consuming part indeed.

My best tip for this stage of analysis is to ask a lot of practical questions and come up with some hypotheses that you need to answer or justify through data. We have to also be mindful of the feasibility of the project, otherwise, you can be more flexible in terms of tweaking your approach towards a more doable one.

Note that you can use the programming language that you are most comfortable with 🙂 I believe that either Python or R has its own advantages and great supporting data packages.

An example from my past project can crystalize this strategy. I was curious about the non-pharmaceutical factors that correlate to the suppression of COVID-19 so I listed out all of the variables I can think of such as weather, PPEs, ICU beds, quarantines, etc. then I began massive research on the open-source data sets.

“All models are wrong, but some are useful” — George Box

Since I did not have a background in public health, building predictive models for this type of pandemic data was a huge challenge. I first started with some models I’m familiar with such as random forest or Bayesian ridge regression. However, I discovered that pandemic typically follows the trend of a logistic curve in which the cases grow exponentially over a period of time until it hits the inflection point and levels out. This refers to the compartmental models in epidemiology. It took me almost 2 weeks to learn and apply this model to my analysis but the result was extremely mesmerizing. And I eventually wrote a blog about it.

The process

If you are working in the Data Science/Analytics field, this is not new to you — “80% of a data scientist’s time consists of preparing (simply finding, cleansing, and organizing data), leaving only 20% to build models and perform analysis.”

Photo by Impulse Creative

The process of cleaning data may be cumbersome, but when you get it right, your analysis will be more valuable and significant. Here’s the typical process I take for my analysis workflow:

1) Collecting Data

2) Cleaning Data

Many more…

3) Project-based techniques

  • (NLP) Sentimental analysis, POS, topic modeling, BERT, etc.
  • (Predictions) Classification/Regression model
  • (Recommendation System) Collaborative Filtering, etc.

Many more…

4) Write up insights and recommendations

Connecting the dots

This is the most important part of the analysis. How do we connect the analysis insights into a real-life context and making actionable recommendations? Regardless of your project’s focus, whether it’s about machine learning, deep learning or analytics, what problem is your analysis/model trying to solve?

Photo by Quickmeme

Imagining that we build a highly complex model to predict how many Medium readers will clap for your blog. Okay, so how’s this important?

Link it to potential impacts! If your post receives more endorsement from claps, it may get curated and featured more often on Medium platform. And if more paying Medium readers find your blog, you can probably earn more money through the Medium Partner Program. Now that’s an impact!

However, it’s not always about profit-driven impact, it could be social, health, or even environmental impact. This is just one example of how you can make the connections between technical concepts with real-world implementation.

Roadblocks

You may hit a wall at some points during the journey. My best piece of advice is to proactively seek help!

Besides from reaching out to friends, colleagues, or mentors to ask for advice, I often found it helpful to search or post questions on online Q&A platforms like Stack Overflow, StackExchange, Github, Quora, Medium, you name it! While seeking for solutions, be patient and creative. If the online solutions have not yet solved your problems, try to think of another way to customize the solution for the characteristics of your data or the version of the code.

The art of writing is rewriting.

When I first published my first data blog to Medium, I found myself re-visiting my post and fixing some sentences or wording here and there. Don’t be discouraged if you notice some typos or grammar mistakes after releasing it, you can always go back and edit!

Since it is our personal project, there’s no obligation on whether you must finish it. Hence, prioritization and disciplines play a crucial role throughout the journey. Set a clear goal for your project and lay out a timeline to achieve it. At the same time, don’t spread yourself too thin since it may cause you to lose interest.

Understand your timeline and capacity! I often push my personal project in a sprint of 2 to 4 weeks to finish during break or the weekends. In order to organize your sprint and track your progress, you can refer to some Agile framework that can be found through collaboration software like Trello or Asana. As long as you make progress even the smallest one, your success shall flourish some day. So keep going and don’t give up!

Closing Remarks

The first step is always the hardest. If you don’t think that the project is ready yet, give yourself some time to fine-tune and share it!

Nothing will be perfect at first. But by shipping it to the audiences, you would know what to improve for later projects — I adopted this principle wholeheartedly from product management perspectives.

I used to be not good at communicating my thoughts structurally and clearly (which I’m still trying to improve), but by pushing myself out of the comfort zone, I have gone extra miles from where I was. I hope this will, to some degree, inspire you to start your first data blog. Believe in yourself, be brave and reach out to me or anyone in your network if you need help along the way!

“Faith is taking the first step even when you don’t see the whole staircase.” — Martin Luther King

Photo by Glen McCallum via Unsplash

By Giang Nguyen

Sourced from towards data science

By

The video-call provider has apologised for sending data to Facebook without users’ permission, showing that we must be vigilant about the tech we use.

A couple of months ago, Zoom was a dull, if successful, videoconferencing app that not many people knew about. Now, it is a household name and an integral part of many of our quarantined lives. We conduct business meetings on it; we chat to our mates on it; some people even have sex parties on it.

Yet there are growing concerns over what it does with users’ data. You may think you are working from the privacy of your own home, but the software is probably sharing a lot more information about you than you realise. Zoom has an attention-tracking feature, for example, which notifies the host of some video calls if participants click away to look at something else. The company has actively promoted this feature to educators, explaining it’s a good way to monitor which of your students is slacking off.

In any article about privacy violations, it is pretty much a given that Facebook will be mentioned. This is no exception. Recent analysis by Vice found that Zoom’s iOS app was sending analytics data to Facebook, even when the user did not have a Facebook account and even though this was not addressed in Zoom’s privacy policy. This data included things such as the user’s location and the device’s advertiser identifier information, a unique ID that lets companies send you targeted ads. On Friday, Zoom issued a statement saying “whoops!’” and announcing it had updated its software to stop sending iOS data to Facebook.

I am not saying that you should boycott Zoom and communicate via carrier pigeon. However, as we are forced to live even more of our lives online, let’s not stop holding tech companies to account. Let’s not stop trying to safeguard our right to privacy. Our civil liberties are most fragile during times of crisis. Governments around the world are already using this pandemic to bolster the surveillance state. If we don’t stay vigilant, our privacy will be lost before you can say “Zoom”.

Since you’re here…

… we’re asking readers like you to make a contribution in support of our open, independent journalism. In these frightening and uncertain times, the expertise, scientific knowledge and careful judgment in our reporting has never been so vital. No matter how unpredictable the future feels, we will remain with you, delivering high quality news so we can all make critical decisions about our lives, health and security. Together we can find a way through this.

We believe every one of us deserves equal access to accurate news and calm explanation. So, unlike many others, we made a different choice: to keep Guardian journalism open for all, regardless of where they live or what they can afford to pay. This would not be possible without the generosity of readers, who now support our work from 180 countries around the world.

We have upheld our editorial independence in the face of the disintegration of traditional media – with social platforms giving rise to misinformation, the seemingly unstoppable rise of big tech and independent voices being squashed by commercial ownership. The Guardian’s independence means we can set our own agenda and voice our own opinions. Our journalism is free from commercial and political bias – never influenced by billionaire owners or shareholders. This makes us different. It means we can challenge the powerful without fear and give a voice to those less heard.

Your financial support has meant we can keep investigating, disentangling and interrogating. It has protected our independence, which has never been so critical. We are so grateful.

We need your support so we can keep delivering quality journalism that’s open and independent. And that is here for the long term. Every reader contribution, however big or small, is so valuable. Support the Guardian from as little as €1 – and it only takes a minute. Thank you.

Feature Image Credit: ‘Let’s not stop holding tech companies such as Zoom to account.’ Photograph: Christian Sinibaldi/The Guardian

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Sourced from The Guardian

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

Sourced from DIGIDAY

While you can’t plan for uncertainty, you can prepare for it. The Advertising Association is encouraging the industry to plan for Brexit as the risks of the UK leaving the EU without a deal on 31 October 2019 are high.

In its remit of representing the interests of the UK advertising industry, the Advertising Association has brought together key pieces of information to ensure businesses have contingencies in place to continue receiving personal data lawfully in the event of a no-deal Brexit. This is intended to provide guidance, and does not replace legal advice.

The UK’s data protection regime is currently governed by the EU’s General Data Protection Regulations (GDPR) and the UK’s Data Protection Act 2018 (DPA 2018). If your organisation receives personal data from the EEA you will still need to abide by both GDPR and the DPA 2018 even after Brexit.

Assessing data adequacy
As the UK is currently a member of the EU, there are no restrictions on the flow of personal data and other EEA Member States. Article 45 of the GDPR states that the European Commission needs to assess the relevant country’s laws to determine whether they are essentially equivalent or “adequate” to that of EU ones.

The UK has announced that it will allow the flow of personal data to the EEA regardless of a deal being in place and will recognise existing European Commission data adequacy decisions. However, the EU has not yet made a similar commitment towards the UK. This is because on leaving the EU, the UK will become a ‘third country’. And while the UK remains an EU member, the European Commission will not conduct this assessment. Unfortunately, this means if we leave the EU without a deal we will not have a data adequacy decision in place to facilitate the free flow of personal data from the EEA.

Standard Contractual Clauses
In the absence of an adequacy decision, GDPR states that personal data can be transferred to a third country or an international organisation if there are appropriate safeguards. There are a number of recognized safeguards, but most appropriate to businesses are the implementation of Standard Contractual Clauses (SCCs).

SCCs are a standard set of contractual terms and conditions for the transfer of personal data which both the data exporter and the data importer enter into. They include contractual obligations that help to protect personal data when it leaves the EEA and ensure compliance with GDPR. SCCs only relate to the transfer of personal data, so they can be incorporated into a wider contract that covers other business terms. One of the key benefits of using these SCCs is that they are approved by the European Commission.

Binding corporate rules
If you are a multinational operating in the UK and in one or more EEA country, then Binding Corporate Rules are required to transfer personal data between the different parts of the Group located in the UK and the EEA.

US Privacy Shield
If you send data to a US Privacy Shield organisation, the Privacy Shield participant will need to update their public commitment to specifically reference the UK, in addition to the EU. There is further information on the US government’s Privacy Shield website. In addition, the ICO has published guidance for organisations about international data transfers.

Data Protection Lead Authority
If the ICO is your lead Data Protection Authority, you may need to review your operations to assess whether you can still have a lead authority and benefit from the one-stop-shop following Brexit.

Appointing a data representative.
If you are a data controller or processor that is subject to GDPR but not established in the EEA – as will be the case when the UK leaves the EU – you have an obligation to designate a data representative based in the EEA. This representative will be the go-to person to deal with individuals and DPAs in the EEA. The UK plans to oblige non-UK controllers who are subject to the UK data protection framework to appoint representatives in the UK if they are processing UK data on a large scale.

It’s important to regularly check the GOV.UK website for updates. The ICO has a page dedicated to Brexit that covers the implications for data protection and data transfers in more detail and its SCC tool provides template contracts. If you need more information about your obligations and what you need to do to comply, we recommend seeking legal advice.

For more information on matters relating to Brexit, visit the Advertising Association website: https://www.adassoc.org.uk/policy-areas-category/Brexit/

Sourced from DIGIDAY

By Paul Matthews

In 2018, the world has been shaken by the usage of big data: the Cambridge Analytica scandal, which was related to the allegedly illegal buying and selling process of data points and data-related pieces from the British company, has put data science into the spotlight of the “mainstream business” world. After this scandal, in fact, data has surpassed oil as the most valuable asset on Earth. Let’s analyse why and, most importantly, how this has happened.

Data Points: A Commercially Powerful Numerical Value

For “data point”, we intend a numerical value which, when associated with a specific entity (i.e. a person, a company), combines preferences, comments and tastes (from a numerical perspective) in order for a software to automatically elaborate them. The power of data points stands in the fact that, when properly analysed, they could give thorough insights on a particular user’s preference on a specific topic. The “exploitation” of Facebook searches on the Brexit topic, for example, was elaborated using data points to provide highly tailored ads to the people who were either searching for “leave the UK” and related keywords. Although this may sound slightly political, it was actually confirmed by Cambridge Analytica itself last year after they (and Facebook) were fined for over $2 billion for buying and selling private pieces of information (data points).

Data Science: An Enterprise Niche Sector Going Mainstream

The possibility of creating tailored ads based on numerical values has intrigued business owners worldwide to the point in which they decided to open data science-related divisions in companies which weren’t exactly at an “enterprise” level. Data elaboration, acquisition, science and Python development professional figures have been recruited in small and medium companies worldwide massively, in the past 7 months. Despite a specific GDPR section strictly regulating data acquisition and processing, data scientists have definitely “gone mainstream” in the recent past.

From fintech to eCommerce, to pure lead generation, the usage of data science has become a constant in 2019.

Some Business Sectors Have Been Getting More Results Than Others…

As mentioned above, data processing and science have been used by a variety of businesses in the past months. Fintech and real estate have been the most successful ones, in terms of lead generation tailored onto data. Sectors like bridging loans, development finance and similar have seen a net 35% increase in organic investment in terms of hiring Python developers who were able to process such delicate data to prepare targeted, tailored and highly convertible ads for social media channels. Lead generation has become very dependant on data in the recent past.

To Conclude

The usage of data in 2019 has definitely become a mainstream procedure. In the nearest future, we can safely say that GDPR rules will become even more strict: with more specific regulations on the acquisition and storing, data is still far away from being fully regulated.

By Paul Matthews

Paul Matthews is a Manchester-based business and tech writer who writes in order to better inform business owners on how to run a successful business. You can usually find him at the local library or browsing Forbes’ latest pieces. Paul is currently consulting a bridging loans company in Manchester.

By

I am lucky to talk to key executives from hundreds of companies each year. Inevitably, the conversation typically rolls around to the effectiveness of digital marketing. The one common link in each of the conversations is understanding if their marketing is effective and interpreting what it means. In some cases, the organization has never received a report. In others, they get numbers but don’t know what they mean or how to decipher them.

If you are paying someone (an individual or an agency) to execute a digital marketing initiative, then you should expect to receive regular updates, reports on progress and interpretations of what the data is telling you and what should — or should not — be done about it. If you are missing this information, there are two possible solutions: Meet with your provider and ask for reporting data and recommendations to be delivered consistently, or find a new provider.

Understanding your campaign performance is critical in order to make decisions, allocate budgets and understand your customers and their needs. Let me explain in greater detail.

1. Search Engine Marketing: Paid and organic efforts relating to search marketing are able to provide key insights that can boost your page visibility within your specific sector, improving your page rankings while creating a better experience for your customers. As it relates to your paid search marketing (SEM or PPC), you will want to know what terms your customers are using, as well as which of your keywords has the strongest click-through and conversion metrics. Additionally, ask for reporting on ad group and ad copy performance, site links and call extensions. This will help you better understand your customers and what they want from you while providing insights that can be applied to other areas of your marketing. You should review this information monthly with your contracted provider.

2. Website Optimization: With an SEO contract, you can expect to see regular reports on your website performance in relation to your search engine rankings — how and where you are showing up on Google, Yahoo or Bing search results pages. The actual report may vary by contract, but at a minimum should include a review of your website speed on mobile devices, your current ranking and any change in your ranking for 5-8 keywords, identified technical errors and a summary of what work has been completed to improve in these areas.

3. Video (pre-roll, streaming, promoted): Video marketing has a little different report and KPI structure. With this type of advertising, the goal is typically to increase awareness or evoke some type of emotion. That is difficult to measure in clicks. When you are looking at performance metrics as it relates to video, ask for the video completion rate (VCR) and total time played in addition to any attributed clicks or conversions. This video data will let you know how effective your message is as well as if you are targeting the right audience within your ad buy.

4. Online Display Ads: While many professionals within our industry provide reporting on display ad impressions served and click-through rates (CTR), they really do not tell us the whole story. Request reporting data on ad performance by message and size, conversion metrics and website analytics data that will indicate the quality of the click. In today’s marketplace, it is easy to buy clicks and flood a website with cheap traffic. You will want to ensure that you are paying for quality web traffic, not just quantity.

5. Email Marketing: Reporting on this activity is more straightforward than other digital aspects, mostly because it is more of a standardized service. Ask for a summary for each email sent. It should include the date and time it was deployed, total sends, total opens and reads, number of clicks (and on what links), as well as any results from A/B testing of subject lines and content.

6. Social Media Marketing: Depending on the scope of services of your social media contract, your contractor should be providing a monthly summary of their activity and the results. If the intent is to boost your page engagement, the report should include posts made, activity for each post, change in page engagement over the previous month and data on paid activity. If you are trying to promote an event or sell a product, the report should also include hard numbers on the registrations, sales or leads attributed to the campaign efforts.

7. Website Insights/Usability: The goal of a paid online campaign is to grow your business. Looking beyond impressions and clicks will tell you how well your campaign is working for you. Look for key indicators, such as time on site, pages per visit and new visitors. These data points will let you know how good the quality of traffic is (how many pages they are looking at and for how long). They will also provide insights as to what pages of your website need attention through better/more content or flow.

The data collected from your marketing campaigns provides valuable knowledge. Accessing this information, understanding its meaning and applying the insights will propel your organization further, faster and with lower acquisition costs.

Feature Image Credit: Getty

By

Korena, the Founder & CEO of KeyMedia Solutions, applies 25+ years marketing experience to drive a startegy first approach

Sourced from Forbes

Are retailers hearing the call of mobile?

A recent report by Forrester found that smartphones were used in more than one-third of U.S. retail sales in 2018, from product research to checkout. For retailers looking to convert greater mobile sales, they might want to reevaluate their social media advertising.

According to a Think With Google survey, 51% of smartphone users purchased an item from a different company than originally intended, due to messaging appearing exactly when they needed it. That suggests social media advertising campaigns could attract new customers, if deployed strategically. Designed to help retailers capitalize on this opportunity, marketing platform SmarterHQ launched an Ad Personalization program on Tuesday morning.

In order for brands to acquire and retain valuable customers, they must have a personalized, cross-channel strategy that spans ad platforms,” said Michael Osborne, president & CEO at SmarterHQ. “But until now, targeting within these platforms hasn’t been comprehensive enough. Syncing first-party data to power highly relevant ads often requires extra manual work and IT resources, which has hindered these efforts.”

The program builds on SmarterHQ’s existing behavioral marketing offering, which centers on collecting omnichannel data to inform brand messaging. Through Ad Personalization, the same omnichannel analysis can be integrated with the user’s Facebook and Google advertising to create individualized and customized campaigns. These can then work in conjunction with email, web and mobile pushes that the user already coordinates through SmarterHQ.

But SmarterHQ isn’t the only company taking advantage of the growing emphasis on social advertising and the new data technology available. At Pattern89, an artificial intelligence (AI) platform for digital marketers, data from all of its customers is anonymized and run through the company’s algorithms. This turns more than 100 billion impressions into 2,900 dimensions of analysis that are available to all users.

Pattern89 Computer screen
The Pattern89 platform is popular with e-commerce brands looking to roll out new campaigns every few days. This made possible by AI, which is able to process data and generate new recommendations daily.
CREDIT: Pattern89

“One footwear retailer wouldn’t see the results of another footwear retailer because the machine doesn’t look at the data in that way,” said RJ Talyor, CEO and founder of Pattern89. “Instead, it looks at all of the red shoes, or all of the ads that are targeting women between the ages of 17 and 23. It anonymizes all this data, runs the analysis and identifies where the biggest opportunities are for you.”

Users of the program are then presented with a daily to-do list to optimize advertising performance, which Talyor estimates can be completed in five minutes. A new feature introduced this week, Gemini, enables users to automate the daily to-do list by clicking a “do it for me” button. Then there is the Creative Planner program, which makes broader advertising strategy recommendations based on AI learnings.

Artificial intelligence is becoming more common in retail; Salesforce projects that the percentage of retail and consumer goods marketers that are leveraging some form of AI will increase to 70%, from 20%, in the next two years. It also found that, during the 2018 holiday season, AI-powered recommendations yielded 14% higher, on average, order value.

Nevertheless, many retailers are still resistant to AI findings. As Pattern89’s algorithm looks at data from across industries, users receive insights collected from unexpected places; the same customer might buy both a pair of shoes and a mattress, revealing trends that work across contexts. But these recommendations can seem counterintuitive or untrustworthy, such as when one woman’s brand was told it should target men in its advertising. The brand chose not to follow the suggestion, but Talyor believes that not trusting AI is a mistake.

“There’s no bias in the machine; it’s looking for the lowest opportunity,” said Talyor. “It requires humans to intervene — and sometimes humans are unwilling to part with their intuition and their experience. But others are and when they do, they find untapped pockets of opportunity.”

Want more?

How Chatting on Social Media Could Bring Big Business Gains

This Mobile Platform Wants to Help Everyone Become an Influencer

This Acquisition Means More Data and Actionable Insights for Retailers

Feature Image: Salesforce found that 87 percent of consumers begin their shopping journey with digital tools, such as smartphones.CREDIT: Glenn Hunt/EPA-EFE/Shutterstock

 

Sourced from FN

By Korena Keys

I am lucky to talk to key executives from hundreds of companies each year. Inevitably, the conversation typically rolls around to the effectiveness of digital marketing. The one common link in each of the conversations is understanding if their marketing is effective and interpreting what it means. In some cases, the organization has never received a report. In others, they get numbers but don’t know what they mean or how to decipher them.

If you are paying someone (an individual or an agency) to execute a digital marketing initiative, then you should expect to receive regular updates, reports on progress and interpretations of what the data is telling you and what should — or should not — be done about it. If you are missing this information, there are two possible solutions: Meet with your provider and ask for reporting data and recommendations to be delivered consistently, or find a new provider.

Understanding your campaign performance is critical in order to make decisions, allocate budgets and understand your customers and their needs. Let me explain in greater detail.

1. Search Engine Marketing: Paid and organic efforts relating to search marketing are able to provide key insights that can boost your page visibility within your specific sector, improving your page rankings while creating a better experience for your customers. As it relates to your paid search marketing (SEM or PPC), you will want to know what terms your customers are using, as well as which of your keywords has the strongest click-through and conversion metrics. Additionally, ask for reporting on ad group and ad copy performance, site links and call extensions. This will help you better understand your customers and what they want from you while providing insights that can be applied to other areas of your marketing. You should review this information monthly with your contracted provider.

2. Website Optimization: With an SEO contract, you can expect to see regular reports on your website performance in relation to your search engine rankings — how and where you are showing up on Google, Yahoo or Bing search results pages. The actual report may vary by contract, but at a minimum should include a review of your website speed on mobile devices, your current ranking and any change in your ranking for 5-8 keywords, identified technical errors and a summary of what work has been completed to improve in these areas.

3. Video (pre-roll, streaming, promoted): Video marketing has a little different report and KPI structure. With this type of advertising, the goal is typically to increase awareness or evoke some type of emotion. That is difficult to measure in clicks. When you are looking at performance metrics as it relates to video, ask for the video completion rate (VCR) and total time played in addition to any attributed clicks or conversions. This video data will let you know how effective your message is as well as if you are targeting the right audience within your ad buy.

4. Online Display Ads: While many professionals within our industry provide reporting on display ad impressions served and click-through rates (CTR), they really do not tell us the whole story. Request reporting data on ad performance by message and size, conversion metrics and website analytics data that will indicate the quality of the click. In today’s marketplace, it is easy to buy clicks and flood a website with cheap traffic. You will want to ensure that you are paying for quality web traffic, not just quantity.

5. Email Marketing: Reporting on this activity is more straightforward than other digital aspects, mostly because it is more of a standardized service. Ask for a summary for each email sent. It should include the date and time it was deployed, total sends, total opens and reads, number of clicks (and on what links), as well as any results from A/B testing of subject lines and content.

6. Social Media Marketing: Depending on the scope of services of your social media contract, your contractor should be providing a monthly summary of their activity and the results. If the intent is to boost your page engagement, the report should include posts made, activity for each post, change in page engagement over the previous month and data on paid activity. If you are trying to promote an event or sell a product, the report should also include hard numbers on the registrations, sales or leads attributed to the campaign efforts.

7. Website Insights/Usability: The goal of a paid online campaign is to grow your business. Looking beyond impressions and clicks will tell you how well your campaign is working for you. Look for key indicators, such as time on site, pages per visit and new visitors. These data points will let you know how good the quality of traffic is (how many pages they are looking at and for how long). They will also provide insights as to what pages of your website need attention through better/more content or flow.

The data collected from your marketing campaigns provides valuable knowledge. Accessing this information, understanding its meaning and applying the insights will propel your organization further, faster and with lower acquisition costs.

Feature Image Credit: Getty

By Korena Keys

Korena, the Founder & CEO of KeyMedia Solutions, applies 25+ years marketing experience to drive a strategy first approach.

Sourced from Forbes

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With thousands of marketing technology vendors to choose from, most companies are using multiple products to meet customers where they are — whether they’re scrolling through Instagram, doing a Google search, checking their email or entering a store. As a result, marketing stacks today may consist of dozens of different technologies and potentially thousands of data streams.

This fractured landscape makes it difficult for companies to get a holistic view of their customer data and analyze marketing performance, but finding a solution is imperative. Visibility is critical to the success of today’s marketing teams — that’s what allows them to optimize strategies, course correct if needed and achieve campaign goals.

Companies have sprouted up to address this need. As the chief marketing officer (CMO) of one such platform that provides a single source of truth for integrating customer data, tracking marketing performance and analyzing return on investment, it is clear that these marketing intelligence tools are entering the mainstream. However, I have found that some marketers remain unclear about their long-term uses.

As customer expectations for personalized engagement evolve, CMOs need to understand how influential trends will affect the way they evaluate the effectiveness of their marketing and how marketing intelligence can help them stay one step ahead. Here are the five trends that I believe are shaking up the marketing industry.

1. Artificial intelligence is moving beyond the hype.

Artificial intelligence (AI) tools are now capable of doing the heavy lifting in processing massive amounts of data. For example, marketers can create continuous data source connections and automatically organize data in real time without specialized technical skills.

When asked “which activities marketing analysts spend the majority of their time on, data wrangling topped the list, along with data integration and formatting,” according to Gartner’s “2018 Marketing Analytics Survey.” Marketing intelligence tools can help leverage these AI capabilities specifically for marketing use cases, without marketers having to build tools from scratch or adapt general enterprise solutions for their needs.

These tools enable marketers to integrate all of their data, have visibility into it in real time and take action to pivot journeys or campaigns as they’re happening. I believe AI will soon get even better at not only surfacing insights but also at providing smarter recommendations and empowering marketers to take action on them.

2. Marketing analytics is becoming democratized.

Barriers to entry are dissolving when it comes to marketers accessing data insights. The technology solutions emerging today are leaning into marketers’ skill sets, allowing them to focus on analytics without requiring the technical know-how to create complex models.

Marketers can use marketing intelligence to acquire a deeper level of insight by connecting the dots across all customer engagements, including email marketing, paid advertising, web traffic and more, rather than being limited to data from a single channel. With these tools, you can gather recommendations about how to structure programs and spend budgets based on historical data from your own company and industry benchmarks.

3. Martech and adtech continue to converge.

As marketers have sought ways to create a seamless experience throughout the customer journey, marketing and advertising technologies have begun to merge. This trend has been happening for quite some time, but analytics has remained a challenge. Marketing intelligence tools are bringing all of that information from across the customer journey — from paid advertising to email and e-commerce — together in one place.

Regardless of whether an ad platform is part of a larger enterprise marketing technology solution or is a standalone vendor, brands can leverage marketing intelligence to bring everything together in one place to analyze performance, from first-party data about known customers to anonymous data and from the granular to the aggregate.

4. Brands and agencies are banding together.

Traditionally, brands allocated a budget for agencies to spend on advertising, and the brand would take it from there. These days, I find that brands want more transparency into where their ad spending is going and a better grasp of how it’s performing. Brands and agencies are often taking joint ownership of data and working together to drive customer engagement not only through advertising but also throughout the entire sales cycle.

As agencies and brands become strategic partners, they are also converging on the technologies they use. Marketing intelligence can help provide a single source of truth for both agencies and brands in order to collaborate on a standardized platform.

5. Customers are demanding personalization.

Companies that sell products directly to customers are known as business-to-consumer (B2C), while those that sell to other companies are known as business-to-business (B2B). Many companies are both. For example, Ticketmaster sells tickets for live events directly to customers and also has business relationships with venues, entertainment agencies and sponsors.

(Full disclosure: Ticketmaster is a client of Datorama.)

In the past, business buyers didn’t require the same level of personalized engagement as consumers in the market for shoes, chairs and TVs. Now, B2B marketers are expected to step up their personalization strategies. That means reaching beyond traditional demand generation sources, like a company’s website, to channels like LinkedIn and Instagram.

In light of this shift, I believe it’s more important than ever for B2B brands to employ marketing intelligence in the same way as B2C companies do. These companies can gain a unified view of their data, connect every part of the sales funnel and enable highly personalized marketing campaigns.

The Next Competitive Advantage

Marketing leaders who feel overwhelmed by the challenge of unifying disparate data streams are in good company — this is the same quandary many of the largest brands in the world are tackling. While it’s easy to succumb to analysis paralysis, companies that get ahead of the pack in achieving true visibility are often better poised to win.

CMOs already invest nearly a third of their budgets on marketing technology. Now it’s time for marketers to make sure they’re able to make sense of all the data they collect.

Feature Image Credit: Getty

By 

Chief Marketing Officer at Datorama. Former CMO at Synthesio. Helping marketers navigate their data woes each step of the way.

Sourced from Forbes

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Business Intelligence software collects, stores and analyses data, turning it into useful information to help businesses make better decisions.

As the internet is used by companies to discern and target potential consumer trends, the desire to collect vast amounts of data has grown exponentially. Making sense and use of the data collected requires a system for collecting, storing and analysing it.

If you’re old enough to remember, or have seen the original “Star Trek” series, you may recall that Captain Kirk and others could merely ask a computer a question, and the computer, after some blinking of lights and strange beeps, would deliver an answer either by voice or print-out.

With Business Intelligence software, an employee at a desk with a computer merely needs to type in a query, and within a much shorter time and without the strange beeps, an answer is displayed.

What Is Business Intelligence?

Business Intelligence, or BI, is the term given software applications that change raw data into meaningful and useful information to help businesses make better decisions. The term Business Intelligence actually came into use around the 1950s. It grew out of early computing technology called ‘decision support systems.’

Business Intelligence systems have grown more powerful since then, due to increased data collection and greater storage capacity, and the burgeoning use of smartphones and wearable devices that all help in data collection.

In the 1950s, companies didn’t have access to smartphone metadata, internet usage records, social media activity, or “smart home” assistants like Alexa and Echo.

How Does Business Intelligence Work?

The main purpose of Business Intelligence platforms is to sift through data to find patterns and trends.

There are usually four components to BI software:

Data analysis, the reports from which can influence company direction, product line ups and even hiring decisions;
Data mining, which is the analysis of large sets of data to find patterns and correlations;
Text analytics software, which sifts through unstructured textual data to find patterns and is used to analyse sentiment in social media posts or online customer feedback, and
Business analytics, which has its own three main forms:

  • Descriptive, which describes data you already have, to look to identify trends and relationships inside of it, like page views, and even sales numbers within a specific department.
  • Predictive, which searches for a correlation between a single unit or factor, and the features pertaining to it to find some correlation between different sets of data. This allows companies using it to predict future patterns from past trends, and is according to experts the fastest growing form of analytics.
  • And decision analytics, which helps companies make decisions by analysing not only past data, and extracts trends, but also looks at future conditions such as manufacturing trends, or what the market it going to be like in a few years. Decision analytics even makes predictions on shortages of resources, to help map out the safest course for a company to take over a number of years.

The main way to make use of all the info available is “data visualization,” which also is growing sub-field in Business Intelligence. Data visualization is the graphic display of results of your data mining efforts, or analytics, and can update in real time.

There are three main types of data used in Business Intelligence functions:

  • Structured, which resides in a fixed form, is labelled, such as with a name and other information collection boxes on websites, or address fields for shipping information, has a header, and can be put into a database program like Excel, and you can query it or search it with a computer, so it can be analyzed.
  • Semi-Structured, which has elements of both structured and unstructured data.
  • Unstructured, which has information that can’t be easily read by computers, and is difficult to organized in traditional databases, because it can’t be stored or collated in rows or columns. This is the most common form of data found on the internet.

Data usually resides across different systems, such as CRM programs, marketing automation systems, customer information – such as consumer sentiment – or reviews in social media platforms.

The first step in BI is to take inventory of data your company already produces, and figure out how you can analyse it and how you can cross-reference them.

The more often data is extracted and analysed, the more up-to-date analytics reports from the data will be.

A method of collecting open-source software utilities to facilitate use of a network of many computers to solve problems that involve massive amounts of data, Apache’s Hadoop, is used with Business Intelligence by large customers such as Facebook (FBGet Report) , which customizes it, and Ebay (EBAYGet Report) .

Why Is Business Intelligence Important?

Business Intelligence Trends

The trends in BI seen most important in a survey of 2,679 users, consultants and vendors by BI-Survey.com were master data and data quality management, data discovery, and self-service BI.

According to the survey, “While master data and data quality management builds a strong foundation for handling data, the significance attached to data discovery and self-service BI shows that the empowerment of business users is a consistently strong trend.”

The same survey also found that agile BI development and advanced analytics and analytics teams are increasing in importance; agile BI development is connected to a cooperative approach between lines of business and IT, while advanced analytics expresses the need for businesses to use data in a more beneficial way. Advanced analytics also includes machine learning, tightly interconnected to the sphere of artificial intelligence, the survey said.

Meanwhile, real-time analytics and mobile BI appear to be decreasing in importance, the survey discovered. Either current tools and systems aren’t able to provide these kinds of applications, or priorities have changed, the survey suggested.

Feature Image Credit: Shutterstock

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Sourced from TheStreet