Re-Thinking Sales Attribution: How Advanced Analytics Enable Accurate Attribution Models that Optimize Customer Experiences and Drive Sales

Armed with instant information and the expectation to shop whenever and however they choose, today's consumers are compelling retailers to enable seamless experiences across multiple touch-points and diverse paths to purchase.

The routine use of diverse channels and paths is also leading retailers to re-think how they assign credit for a sale. What was once a simple decision (i.e. when purchases were typically influenced, completed and fulfilled in one location or channel) is now a complex challenge for retailers who want to reward and thus incentivize each location, channel and agent that drives the transaction and contributes to the customer's satisfaction.

Simple solutions prevail—and fall short

To some degree, retailers recognize the importance of addressing this challenge: One recent study found that only 16% of retailers believe that sales attribution is irrelevant in omni-channel transactions. Yet the study also found that only 16% of retailers would even split the credit between the store and online; a much greater percentage would simply assign full credit to one channel or the other .

Many retailers follow a similar "keep-it-simple" approach when deciding if and how to credit separate selling and fulfilling locations. The default is to favor the selling location for having done the critical work to engage and convince the customer, on which everything else depends. In contrast, however, some higher-end "showroom" retailers that don't carry a lot of stock in any one location, and that therefore depend heavily on enterprise-wide fulfillment, may justify giving more credit to the fulfilling location.

Limitations and liabilities

These straightforward "either/or" or "50-50" attribution methods have obvious limitations:

• If only the associate and location that makes the sale gets full credit, a separate fulfilling agent at another location may have less incentive to follow-through in a way that meets the customer's expectations.

• Evenly splitting credit with both the online and store channels doesn't necessarily reflect the relative value of each agent in the process. And when credit is split between two physical locations in a high-end chain, the retailer may end up paying twice on commissions—which may be good politics but is always bad accounting that harms the bottom line .

• Simple attributions are even more even more problematic in complicated purchase scenarios involving multiple touch-points. When there are fewer incentives than the number of agents required to meet the customer's expectations, there is a greater risk of breaking the value chain.

These are critical but preventable errors. Since the retailer's goal is not just to gain a one-time conversion but to build loyalty for repeat sales, recognizing and rewarding each agent proportionally can help achieve it by ensuring that customers enjoy a great experience at every step in the shopping process, from initial engagement to merchandise receipt.

The power and value of analytics

Advanced retail analytics can make it not only possible but relatively easy, in ways that manual or legacy systems cannot. Using the right analytics tools, retailers can measure customer and transaction activity at a granular level within and across channels, answering key questions such as:

• Where was the primary point of customer engagement?

• How many touch points thereafter have influenced the customer's decision to buy?

• Were separate locations and agents responsible for fulfilling the order and therefore for ensuring the customer's ultimate satisfaction?

• What was the incremental impact: How much influence did each touch-point and agent have on the customer's purchase decision and satisfaction?

• Which points in the sales and customer satisfaction cycle can be measurably improved—and how?

Analytics enable retailers to analyze the role and impact of every agent in the customer's journey who is responsible for driving the sales cycle, to develop attribution models that are accurate, effective and fair. Retailers can create scorecards with columns for each channel and assign scores for a wide range of KPIs, such as conversion assists based on engagement and dwell times, conversions based on last touch or click, timely fulfillment, and much more .

Analytics also makes it possible to develop hybrid attribution models that meet the specific needs of each business. For example, one national footwear retailer gives their selling stores financial credit but assigns unit credit to the fulfilling store.

Going further and doing more

All of these advantages are amplified when retail analytics are deployed as part of a singular commerce platform that unifies the retail environment and enables one view of transactions, customers, orders and merchandise across the enterprise. Truly seamless customer experiences are only possible in these conditions.

Analytics-based attribution models are also more effective when they are supported by policies that encourage and mandate teamwork. One national luxury goods retailer does this by grouping separate fulfilling locations and analyzing sales performance by region, to encourage a one-for-all approach while preserving a sense of competition. In another national chain focused on sporting goods, any location that does not fully comply with fulfillment requests is denied the opportunity to make them of other stores.

Yet whatever else is done, the use of analytics for attribution modeling should now be considered essential for any retailer seeking to continually drive performance and customer satisfaction through multiple touch-points, for improved competitive advantage and bottom-line results.

For more information on the role and use of analytics in sales attribution modeling, contact Aptos at info@aptos.com.