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A good customer profile relies on objective information to describe shoppers, often segmenting them around the reasons they choose a company or product.

The term “customer profiles” is sometimes used interchangeably with the phrase “customer personas” or “marketing personas,” but a few marketers — especially those at HubSpot — prefer separate meanings. These folks tend to define “profiles” as data-driven descriptions of actual customer demographics and behaviors while “personas” are composites and generalizations.

In this post, I’ll offer tips for building and using customer profiles.

Define the Purpose

A shocking number of customer profiles (or personas) go unused.

In the past six months, I have worked with a direct-to-consumer ecommerce seller, a brick-and-click retailer, and a small boutique shop. In each case, when I asked if they have customer profiles, the reply was, “We used to, but we don’t use them anymore.”

These three companies had each gone through the exercise of creating a portrait of their most desirable customers. One set of personas had been printed on heavy stock and was pinned to a bulletin board in the marketing department like art. But they were not used.

Your ecommerce customer profiles should be tools with a purpose. Before you collect oodles of demographic details or piles of psychographic insights, you need a clear direction.

For example, customer profiles can help choose advertising vehicles. They can make it easier to identify customer segments for email or direct marketing. They can be used to guide ad copy and identify the key reasons a shopper buys. And they can be a source of ideas for content marketing.

Know why your ecommerce business needs customer profiles and how those profiles will be integrated into your various workflows. Will profiles be used, say, for customer acquisition or for building customer relationships?

Will Customers Benefit?

As you contemplate how your ecommerce company should employ customer profiles, try to figure out how you can help your customers.

Many products solve a problem. A fellow with a dry and coarse beard might want Beardbrand’s Tree Ranger beard oil because it will hydrate his beard and make it feel soft.

Understanding why a shopper wants to buy beard oil should make it easier to sell.

A female planning to attend an eighties-themed rave next weekend could be interested in BLANKNYC’s “Girl’s Night Out Skirt” because it looks like leather, but it is made with polyurethane and viscose. Thus, it is suitable for a thoughtful vegan circa 2020.

Realizing that your customers want vegan materials helps you develop and sell products.

Insights about why your customers buy a particular product and why they buy it from your store are among the most important benefits of developing customer profiles, so don’t miss out.

Use Objective Data

Customer service representatives can provide interesting information about your shoppers, including, in my experience, some amazing anecdotes.

Interviewing customer service reps can go a long way towards your company’s buy-in for profiles.

Don’t stop, however, with subjective information. Take the time to collect and analyze objective data about who your customers are, what motivates them, and how they behave.

For example, I’ve observed marketers at a multichannel merchant export every customer record from its database as a CSV file — more than 22,000 total rows.

This file was opened as a Google Sheet. Using filters, the marketing team narrowed the list to just 27 shoppers who had been the most loyal and most valuable to the company.

It turned out that all 27 had interacted with the same customer service representative on several occasions.

Next, they found this same person interacted with the top 1 percent of customers 50 percent of the time.

In other words, one person had a huge influence on sales. It was a stunning find for the business. And it was only possible with objective data.

Use Affinities, Too

Notwithstanding the benefits, season hard, objective facts with customer responses, feedback, and social media posts that indicate opinions, preferences, and other affinities.

You can gather this information with customer surveys, emails, or even after-hours voice messages.

The merchant that identified its 27 best customers also looked those folks up on Facebook, LinkedIn, and other social media sites to learn if they had common likes and dislikes. This, too, is a good source of info.

Iterate

Finally, customer profiles are not static. Rather, they should be reviewed and updated regularly — as often as once a year. The key, however, is first to define the purpose, such as for new product launches or major marketing campaigns.

 

 

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Sourced from Practical Ecommerce

By  Derek Andersen 

Forbes recently stated that 80% of enterprise companies are investing in artificial intelligence (AI) solutions today. AI is a machine’s ability to imitate intelligent human behavior by perceiving a set of inputs and processing that information in order to reach a desired outcome.

In the martech space, companies are utilizing AI to build customer profiles, resulting in more precise ad targeting as well as unprecedented customization. Below are three recent examples of how companies are using AI to build customer profiles and drive revenue.

1. Teleflora Uses AI to Deliver Personalized Product Recommendations

A recent article in Direct Marketing News details how Teleflora, a leading floral arrangements vendor with more than 15,000 member florists in the US and Canada, uses AI to build customer profiles and provide a personalized touch.

Source: Teleflora.com

When Tommy Lamb, Teleflora’s new director of CRM and loyalty, joined the company, he immediately realized their marketing strategy was underdeveloped. Since customers typically only used Teleflora at spread-out points in the year, the company needed strong remarketing and customer service to build brand loyalty. But rather than providing personalized offers, they only utilized a few generic holiday email campaigns.

A retail marketing platform called Bluecore gave Lamb and Teleflora the AI capabilities they needed in order to execute a three-pronged personalization plan:

  1. Teleflora first created more comprehensive customer profiles by combining their product data with their individual customer data.
  2. Next, Teleflora paired Bluecore’s machine learning capabilities with these comprehensive profiles to anticipate the future purchases of various audience segments.
  3. Finally, Teleflora integrated advanced analytics, allowing them to identify best-selling items and other purchasing trends.

This AI strategy allows Teleflora to accurately anticipate customer needs. They can target customers who are ready to buy and make personalized recommendations, driving customer loyalty and ROI. Then, their analytics solution allows them to view the results and promote high- or low-performing products accordingly.

2. BMW Leverages AI to Personalize Ad Spend and Lower its Cost-Per-Acquisition

In order to execute a recent campaign, BMW Mini worked to connect and organize its data into an actionable format. Its goal: to target adults searching for a premium vehicle who had shown interest in the BMW brand.

Source: BMWBy partnering with ad agency Universal McCann, BMW was able to leverage its first-party data—which included people who had visited the BMW website or were already in their CRM system. BMW used this data to enhance its existing search strategy, ensuring its ads delivered relevant messaging to interested car shoppers.

BMW then utilized an AI solution to optimize the efficiency of its targeted ads. Over time, this solution optimized BMW’s ad targeting so the messaging would reach the right person, based on factors like time of day, previous searches, and BMW website visits. As a result of this strategy, BMW Mini’s conversions tripled and their cost per acquisition declined by 75%.

3. Comfort Keepers Utilizes AI to Target Caller-Ready Audiences

Comfort Keepers, one of the nation’s leading providers of in-home care for seniors, uses AI-powered conversation analytics to understand what happens on calls to each of their 450+ franchisee locations. Since phone calls make up 70% of their marketing conversions, they analyze the calls to determine who each caller is, if they are a quality sales lead (vs. a non-sales call), and if they converted to an appointment or customer.

By using this conversation analytics data, Comfort Keepers is able to fully gauge the success of their marketing efforts and prove it to each of their franchisees. Not only are they able to identify the quantity of the calls their campaigns drove, but they can also understand the quality. For example, rather than simply saying “we drove 2,000 calls this week,” they’re able to identify how many of those calls are potential new customers versus current customers. This gives them a full picture of the ROI from each of their campaigns to each location.

As a next step, once Comfort Keepers understands who converted on their calls and who did not, they can use that same conversation analytics data from AI to retarget their prospects with search, social, and display ads and use good callers in their lookalike campaigns.
Read more at https://www.business2community.com/marketing/how-ai-helps-marketers-build-customer-profiles-and-drive-revenue-02091198

By  Derek Andersen 

Sourced from Business 2 Community