Augmented Analytics for Sales and Marketing: Customer Profiling and Product Adoption

If you’re in sales and marketing, you need IT to empower you with tools that will help you profile customers and drive early adoption of new or additional products and services. For example, if you’re a business analyst in a global telecom company, where competition is fierce, you need to be able to up-sell and cross-sell relevant products or services to expand the company’s “share of wallet” by individual customer and help drive overall revenue and profitability.  

Whatever offers you develop—international dialing plans, unlimited data options, or bundled products and services—you first need to understand your customers before designing appropriate promotions to fit their current and future needs.

A lot is on the line. You can spend precious marketing resources sifting through data to design promotions that miss the mark or don’t deliver the desired results. Also, manual, spreadsheet-based analysis can be complex, time consuming, and error prone when you’re trying to pull all your customer data together to help identify the right prospects and right offers based on previous buying behavior.  

A single source of the truth

One central benefit of augmented analytics is that data—in this case, customer data—is consolidated into a single view for analysis and insight. Not only does this speed the process of designing up-sell and cross-sell campaigns, but it can also increase campaign effectiveness dramatically.  

Based on immediate visibility into customer data, a business analyst in sales or marketing can get to work immediately—by exploring basic demographic data regarding age, location, profession, and education level and then correlating this information to past purchase behavior.

With augmented analytics, you can ask relevant questions. Who are your most profitable customers in a given region for certain product and service offerings? What were the top influencing factors for their past purchases? Why did they choose your services rather than the competition? KPIs to monitor might include the number of existing purchases or online shop visits—or perhaps the average number of calls or text messages sent in a quarter.  

The power of machine learning

In this scenario, machine learning is used behind the scenes to sort through volumes of historical customer data. By detecting patterns in customer profiles and past buying behavior, machine learning algorithms can predict the likelihood of any particular customer or prospect to respond positively to a new offer or campaign. 

Sales product screenshot
This dashboard shows the output of a machine learning-based algorithm for customer profiling—including best target groups and propensity to purchase.

At the bottom of the dashboard, a list of individual customers is ranked according to their propensity to buy. At the top left, target groups are tracked and ranked in the order of their propensity score. Key metrics in this case are the group’s propensity to accept the promotion and its propensity to reject it—which are color coded to aid quick understanding.   

For a business analyst—who is trying to help sales and marketing improve promotional effectiveness—this information is critical. But as highlighted previously, pulling together these “like groups” using spreadsheets and manual analysis would have been virtually impossible in a timely manner.  

Augmented analytics changes the game. Now IT can empower sales and marketing teams with immediate, trusted insight—powered by machine learning—into which customer segments are the best candidates for new promotions. Think of it in terms of investment in your business. Now the business knows which customers are best targets—resulting in higher ROI for campaigns and marketing efforts.   

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