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Behavioral data alone is no longer enough to enhance customer experiences: “experiential” data is needed too

Published: Tuesday, 15 August 2017 by Alyssia Gordon, Customer Insight Consultant

Tracking a customer’s online interactions to improve and personalize their experience is nothing new, but by only collecting this behavioral data, key factors that are influencing a customer’s purchasing decisions are being overlooked.  Without including data about these factors, the performance of both personalization and predictive modeling is compromised, leading to a less than optimal customer experience.

‘Experience’ is becoming an increasingly important differentiator for customers; with Walker citing that by 2020 companies will primarily focus on differentiating themselves through the experience they deliver to customers, while differentiation based on products and price will become less important (1). In addition, research by Forrester Inc. states that “CX leaders grow revenue faster than CX laggards, drive more purchase intent, earn greater pricing power, lower service costs, and reduce regulatory risks”(2).
In order to succeed in a world where customer experience is the main source of competitive advantage, companies must fulfill the difficult task of not only meeting, but exceeding, their customers’ ever growing expectations by delivering exceptional customer experiences. A key facet to delivering an exceptional experience is personalization; not aggregate or persona-based personalization, but at the true one-to-one level. Customers today expect companies to understand their individual needs and personalize the experience they receive accordingly: so getting it right is simply not an option for brands – it’s an essential.

In endeavoring to deliver these personalized experiences, companies have traditionally tracked and analyzed a customer’s interactions with their brand, particularly online where this data has been more readily available. By capturing how a customer behaves online, this data begins to paint a picture of who a customer is and what they are trying to achieve; in turn informing the provision of an experience that would be more relevant to them. However, this data omits key insight derived from digital content a customer sees and ‘experiences’ but does not interact with.

We therefore propose that, to build a truly accurate picture encompassing all the factors that influence a customer’s purchasing decisions, companies must blend and analyze both traditional behavioral data and advanced customer experiential data.

Traditional behavioral data informs some improvements in customer experience

We class traditional behavioral data as all the information that is generated when an individual interacts with a digital channel. This covers everything from mouse clicks and mouse movements to data entered into fields, any selections made and on-site search terms used.

Collecting and analyzing this data can generate some insight on how successful a customer’s experience has been when interacting with a brand. It can be used to identify and rectify instances where the experience is not meeting a customer’s expectations, potentially resulting in them being discouraged from completing a purchase. As an example; repeated clicks on elements that are not intended to be clicked may indicate that customers are attempting to take an action but the call to action is not obvious or available. In this case, a poorly designed digital offering is more than likely to result in a frustrating experience, causing customers to drop out of the transaction before completing a purchase. By identifying this kind of behavior, particularly if it is being displayed across large groups of customers, brands can begin to take some steps towards identifying areas of weakness and improving their customer experience, as well as taking steps to resolve issues for individual customers.

Traditional behavioral data can also be used to power some basic personalization. Monitoring what products customers click on, save to wish lists or add to basket, can be used to drive re-targeting actions. Based on a customer’s interactions with products, suggestions for similar products can be made throughout a brand’s site or reminder emails could be triggered in instances where baskets have been abandoned. This level of personalization may go some way in encouraging purchases, but often lacks an accurate enough anticipation of customers’ needs to dramatically affect customer experience.

So what’s the problem?

These examples demonstrate that although companies can use traditional behavioral data to make some improvements to their customer experience, behavioral data alone lacks the information required to truly understand a customer. Traditional behavioral analytics assumes, for example, that upon page load, all content on that page has been seen. It has no way of taking into account whether a customer has stayed on a page long enough to view a particular offer on a carousel or has scrolled down the page to see a specific promotion that may have persuaded them to complete a purchase.

Behavioral data alone is not sufficient to build a rich and accurate picture of all the factors that influence and motivate a customer to complete a purchase. It omits many elements that may have a real impact on customer behavior, leading to an incomplete and often misleading picture of cause and effect. Considering the gravitational pull of artificial intelligence and machine learning to power enhanced customer interactions and experiences, richer data is set to become ever more critical if organizations are to get full value from these new technologies and move themselves beyond traditional digital analytics.

For a complete understanding of customers introduce advanced experiential data

Advanced experiential data is defined as the digital content a customer sees but does not actively interact with. In Retail, experiential data could be product availability (or unavailability) in a range of sizes and colors. It could be the price quoted for a product or a prominently displayed special promotion. In the Travel and Leisure industry, the data may exist in the form of room availability in a hotel, the number of seats left on a specific flight or the stop over times between transfer flights. Across industries, experiential data encompasses peer reviews, ratings and endorsements that customers see.

Experiential data

 All of these factors influence how a customer may behave and might encourage customers to spend more or less with a brand. By collecting it and analyzing the effects it has, experiential data provides a more powerful source of insight for understanding customers, and thereby being able to improve and personalize their experience, than interaction data alone.

Crucially, experiential data is the key component that sheds insight on why a customer behaved in the way they did. Taking an example in the retail industry; collecting data on whether a customer has viewed a particular offer on a landing page carousel enables analysis of the relationship between promotions actually viewed and subsequent sales value. Any promotions or calls to action not seen by a customer on that particular visit can be discounted as having influenced a purchase. This would determine with a greater level of certainty which offers, if any, have the greatest impact on the products purchased and the total value of products purchased.

Considering an airline’s website as an alternative example, analysis of interaction data may reveal that a large proportion of customers do not buy the cheapest flights. To understand why this is the case, the relationship between other influencing factors viewed by customers such as time of flight, number of seats available, available transfer hubs, extra leg room availability, number of hours between transfer flights and loyalty points earned per flight should be investigated.

Though customers do not directly interact with the factors described, an understanding of whether they were seen and taken into account when considering a purchase is crucial to explaining why a customer may have been influenced to behave in the way they did. The resulting insight provides a richer understanding of what motivates a customer, allowing brands to create more accurate and influential personalization models with the aim of maximizing customers’ positive experiences.

Behavioral and experiential data together power enhanced customer experiences

Without the ability to understand what a customer experienced on their digital channels, regardless of whether they chose to interact with certain content or not, organizations will have blind spots as to why customers behaved in a certain way. By blending experiential data with the behavioral data already captured, organizations can gain much deeper customer insight, improve their models and optimize their personalization, all of which leads to an enhanced customer experience and strengthened competitive advantage.

LinkedIn: Alyssia Gordon



(2) "The CX Transformation Imperative”, Forrester Research Inc., 14th September 2016

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