They Love Me They Love Me Not Data Analytics Can Tell You

Marketers famously trust their instincts when it comes to customers. And while intuition and creativity have important roles to play in marketing, data often tells a more complete story as we attempt to make sense of our digital world. To be a successful marketer, we need good marketing instincts, a creative mind -- and a solid grasp of data and analytics. After all, creativity without conversions equals zero.

From Intuition to Analytics

Consider this example. The marketing chief of a photo-sharing community with more than 100 million members wants to know more about customer churn. Its members interact often with the brand and each other, and they are offered frequent promotions online and through call centers. Which customers become loyal to the brand across a portfolio of products? Which ones drop away? What factors seem to cause churn? Does seasonality play a factor in all this? 

The marketing team logically assumes that customers who buy more also are more likely to become loyal customers. But data tells a different story. Using applied analytics, the team tests a number of factors for churn and loyalty: number of orders, average order size, how recently the order was placed, frequency and number of items ordered per visit. It turns out that the most significant variable to loyalty is not money spent, but the number of items ordered -- and this varies by product and time of year. 

It’s counterintuitive, yet applied analytics gives the marketing team an important insight to drive loyalty. Develop promotional discounts to encourage buying until a threshold of loyalty is reached for that product line, then continue the “conversation” with other types of interactions to further foster loyalty.

These findings are a good example of how data can help us discover new truths when we apply advanced analytics – in this case, using a logistic regression model to explore interrelations among seemingly disparate factors. Data converts guesswork into evidence-based decisions.

Advanced analytics is a pragmatic discipline designed to answer important questions. Which customers, for example, are motivated by discounts or options like free delivery? What drives repeat purchases? Do customers buying on mobile devices differ from those coming through a call center or visiting a website? How do website activity and conversations on social media relate? Advanced analytics illuminates how, when and why customers engage with brands. Findings aren’t always counterintuitive. Sometimes they validate a targeted campaign or offer. 

The point is this: Analytics unlocks the meaning of consumer behavior. It helps us predict the future based on past behavior. And it enables data-driven decision support to improve marketing effectiveness.  

Coming to Terms With Data

Marketing teams are not historically known for their “quant” skills, so using analytics may be a significant change in culture, focus and skill set. But advances in technology and tools bring innovation to the marketer’s doorstep. An open marketing platform can simplify the deployment of the very best of these new tools for data management and analytics. 

The following framework can help create a cohesive view of this changing landscape. These are the steps required to make data and analytics an integral part of the marketing mission: 

Unify your marketing data. Marketing data has three important characteristics: quality, completeness and timeliness. You need to ensure you can gain a cross-channel, cross-platform view of consumer behavior at a highly granular level. In the case of digital, you need access to data dynamically updated as close to real-time as practical.  This data will need to be collected from sources like websites, email campaigns, digital advertising, mobile and social platforms, in addition to call centers, customer relationship management (CRM) and point-of-sale (POS) systems. 

Segment and analyze. Once you have the data compiled, the marketing team can segment and analyze this wealth of information. Common ways to segment the data include analyzing behaviors across different marketing channels, campaigns and devices, as well by geography and demographics. 

Discover hidden correlations. Analytics enables you to find out why customers act as they do by correlating multiple data points to expose hidden relationships. For example, an analysis will reveal what factors correlate to cart abandonment or loyalty. We can test hypotheses on various factors to discover high and low probability. 

Optimize and act. Data is meaningless unless it supports action. Academic insights have little value to the corporate marketing teams under pressure to drive revenue. Once you understand the underlying drivers (or levers) of consumer behavior, you can optimize for these factors by channel, device and consumer segments, and enable 1:1 marketing through personalization.  

Consumers are telling us what they want with every click, call to a call center or visit to a store.  Advanced analytics solutions are here to help marketing teams better understand consumer behavior across channels, tailor campaigns and determine how best to optimize the marketing mix. We have access to more data than ever before. It’s now up to us to strike up a relevant conversation with customers based on what they willingly share with us.  

Creative Commons Creative Commons Attribution 2.0 Generic License Title image by  katerha