Some twenty years ago, the classic example of diapers and beers became the legend that gave rise to a thriving industry: data warehousing and BI.
The folklore goes something like this:
A big retailer mined all of their customer transactions, looking for correlations that would better inform their business. To their surprise, they discovered a direct correlation between the sale of beer and diapers –mainly on Friday afternoons and to men between the ages of 25 and 35. It turns out that men were often being asked to bring home diapers for their newborns and they were picking them up on Friday nights after work. The correlation was found when these same men also picked up beer. What did the supermarket do as a result? They put the beer display next to the diapers, discounted one item but not the other. Sales shot up.
While stories like this have been used to showcase data-driven marketing, we’ve come a long way since. These scenarios were able to identify trends by grouping individuals into blended averages, but the retailer didn’t have a clear picture of the actual person they were dealing with. They were dealing with proxies, using general demographics data to underpin campaign or pricing activities. Since then, “personalization” has taken a whole new meaning and today, distinguishing and recognizing consumers as unique individuals is not only a possibility, it’s an expectation.
The new approaches to personalization and product recommendations work by discovering the relationships among activities of each customer and blending that with contextual data about the customer’s location, sentiments, or life event in order to present the most relevant product, at the exact right time. This includes analysis of historical snapshot information that follows the customer over time and predicts future purchasing behavior as well as data in real-time – for instance – from point of sale.
At Birst, we have seen this kind of product recommendation in both wealth management and gaming.
For instance – one of the largest banks in Canada is using analytics to provide insight to its financial advisors so they can make new product recommendations to their clients at critical junctures of their lives – i.e. when changes in marital status, retirement, income levels or new family members happen. By constantly monitoring changes in customer demographics and correlating the population with similar groups of the same characteristics, financial advisors are able to promote additional products (e.g. life insurance) to existing clients when the time is right and when the client is ready for that type of conversation. The results have been astonishing: the financial advisors with analytics have gathered twice as many assets under their management as the ones that did not have analytics. Since then, the bank has gone to spread the success to all its advisors and management team by putting analytics at everyone’s fingertips.
In another example, a leading children’s educational entertainment company uses Birst to measure player behavior within their products. By understanding user behavior within the application and matching that with sales data from their CRM system, they are able to effectively market new games back into their most active user populations.
Marketing is getting more personal. As analytics evolve to better leverage the data that consumers are actively contributing – such as location, life events or even health information from wearable devices – marketers will become smarter about understanding their customer as people with behaviors, emotions and unique human natures.
Companies that learn about their consumers in richer and more complete ways will gain a significant competitive advantage and find more opportunities to bridge the gap between people and the products and services they offer.
To learn more about how analytics is used by marketers today, download our new e-book.
This blog was originally posted here.