Sales and marketing got their biggest boost in instrumentation from the Internet-driven automation over the past ten years. Browsing, shopping, ordering, and providing customer service on the Web has not only provided tremendous control to end users but also created an enormous flood of information to the marketing, product, and sales organization in understanding buyer behavior by analyzing usage data. Each sequence of web clicks can be collected, collated, and analyzed for customer delight, puzzlement, dysphoria, or outright defection, and the sequence leading to this decision.
Self-service has crept in through a variety of means: IVRs, kiosks, handheld devices, and many others. These electronic events act like a gigantic pool of time-and-motion studies. We have data available on how many steps a customer took, how many products he/she compared, and what he/she focused on: price, features, brand comparisons, recommendations, defects, and so on. Suppliers have gained enormous amounts of data from self-service, electronic leashes connected to products, and the use of IT. If I use the Internet to shop for a product, my click stream can be analyzed and used to study shopping behavior.
How many products did I look at? What did I view in each product? Was it the product description or the price? This enriched set of data allows us to analyze customer experience in the minutest detail.
A number of companies are collecting detailed click-stream data to understand consumer behavior. The motivation for collecting this data came from understanding how customers were using websites, and whether or not there were glitches in the web design that prevented customers from using the sites for the intended function. Whether direct-to-consumer or business-to-business (B2B), it is no secret the online channel is a critical component of business today. Yet analysis of website usage patters identifies users who regularly struggle to complete transactions online and as a result abandon their online transactions midstream. Tealeaf is an example of a product that captures the qualitative details of each interaction.
While clickstream data has provided web designers useful tools to design better websites, it has provided a great source of big data to marketers. As products and promotions are introduced in the marketplace, clickstream data provides marketers with specific information regarding the details of information access. How many customers visited a specific page? What was the sequence of clicking before and after clicking a specific page? How much time was spent on a specific page? If there were actions available based on the information shared, did the audience follow the link or not?
Clickstream data can be correlated with other data. If the clicks were made on a mobile platform, it would be interesting to note the geohash location of the mobile device at the time of the click, and whether or not someone else was also present among the consumer’s buddies. Often consumers are clicking on websites while at the same time watching content on television in a direct response to a commercial and then sharing among social groups. I remember conducting and using a commercial day-after-recall survey mastered by Procter & Gamble (P&G) and Burke Marketing17 in the 1970s and 1980s. The correlations we can make today are a significant enhancement over day-after-recall as we can track consumers with clickstream data in conjunction with the airing of a commercial. In addition, with a series of profile labeling and drill downs, a marketer can analyze the impact by micro-segment and establish an accurate persuasion measure for a commercial using a large proportion of the population.
Shopping is becoming more sophisticated. I recently visited Home Depot and Best Buy to shop for kitchen appliances. By downloading their respective apps on my smartphone, I could collect a fair amount of additional data on the appliance while standing in front of it. I was using the Wi-Fi service supplied by each of these stores, which means they can effectively track my movement within the store and correlate the movement and the use of the app to accurately predict my shopping list. The smartphone identification data, in conjunction with the app use, can allow marketers to correlate shopping with specific customers. If they have access to the audience data from their cable operator, and to browser data from a clickstream supplier, they can correlate a fair amount of consumer information, making a judgment about the level of information that has already been shown to this consumer and whether there are significant pieces of information that have not yet been presented.