December 1, 2020
Ismail Parsa, VP/Head of Data Science
Customers engage with businesses online and offline to fulfill their needs and wants. As they do so, they leave clues about these demands in terms of items searched, reviews read, ads clicked, pages viewed or goods purchased… in turn, marketers leverage this information to retarget these customers. Although this is a fine method of targeting – aka behavioral targeting or BT, there are several issues with this approach.
First, these are actual behaviors; as such they are limited in volume to scale to today's marketing campaign needs. Second, by the time the marketers reach these customers or users (henceforth, customers), it is highly probable that the need/want behind these customer actions has already been fulfilled. [1] This reach gap makes the message/offer untimely and irrelevant to the customer. Finally, even when all appears under control, the offer may still be the wrong one for the customer.
Most marketers today pick out customers for their campaigns as they are organized by product lines or categories (a.k.a. the product-centric approach to customer segmentation and targeting) rather than picking the most relevant campaigns/offers for the customer (a.k.a. the customer-first approach). The revenue pressures from the product categories contribute to this issue. It is not uncommon for the category that makes the lion’s share of the revenue to bully their way onto the largest campaign audience available for targeting.
When marketing-sales campaigns are set to maximize revenue or profitability – rather than the relevance of the offer/message to the customer, the customer needs are not served. If your business goal this period dictates promoting more electronics to your customer base to boost revenues, this is irrelevant to most customers, e.g., Jenny and John might want shoes and gardening supplies, instead.
In a Customer-first undertaking, the relevance replaces revenue. All functional areas of the business (management, finance, production/product, procurement, supply-chain, logistics, finance, sales/marketing, customer service…) are aligned with and work backwards from the customer. The enabler here is the business analytics/machine learning optimized to the customer need/want as opposed to the business goals of maximizing revenue or profitability.
When a business relentlessly pursues Working-Backwards strategies – proactively assessing its customers' needs/wants, consistently and continually adapting its business operations, communications, and connecting with the customers accordingly, then it is eventually perceived as Customer-driven – or so goes the Amazon story. A possible list of Working-Backwards strategies include:
All these and more lie in the data, awaiting to be discovered. When a business makes data-driven magic for its customers, when the customers feel they are treated individually – not as a segment, when their needs/wants are anticipated for and communicated to, then an enduring, rewarding relationship, a silent dialogue, with the customer is activated.
With its data science, engineering, real-time platforming and experimentation capabilities, BLEND360 can help your business make data-driven decisions in this Customer-first economy for substantial growth in customer engagement, loyalty and lifetime value.
[1] This will vary by product category but according to Nielson Norman Group, half of all online purchases occur within 28 minutes of the initial click. 75% occur within 24 hours, 90% by day 12, and the remainder occurs in 4+ weeks after the initial click. This is consistent with the author’s own email targeting experience at Amazon.com.
