value lift through data and algorithms

===

“What is a story without want, without need? Moreover, what is want, what is need, without a story?”

the archive of the forgotten

===

The truth is that people do not really buy experiences, they are attracted to things that seem to offer value, stay engaged when value is offered and, ultimately, buy value <value gained>.

Value lift can occur transactionally and structurally within an experience.

So, let’s discuss maximizing value lift because a company with a robust behavioral data hub is at a massive advantage, if not simply in a unique position to lift value higher for individual products/services/brands as well as for an entire brand portfolio.

Simplistically, a mature behavioral data hub – one that has been accumulating behavioral interaction data for years to create historical patterns across a variety of unique shopping experiences – has the ability to shift shopping experiences from passive buyer to active partner which inevitably leads to “I created the transaction, therefore, its mine” type of shopper experience. By effectively shadowing a shopper’s preferences the shopper becomes an actual contributor to the choice and final decision <as well as with all the steps in the process>. It’s actually a derivative of the “creation economy” in that instead of being sold something, a shopper creates the choice they desire. From a psychological perspective the mature behavioral data hub mediates a variety of subconscious factors shaping the final decisions which contributes to a sense they have actually customized the decision, if not the product itself. This subtle navigation creates a stronger emotional attachment to the decision and increases the actual value to the shopper.

That said. The true value lift with a well-designed Human Algorithm, grounded in a mature behavioral hub, is found in a shopping experience steering away from ‘compromise’ and leaning in to “integrating” preferences. Compromise is naturally reductive in nature. Integrating is naturally expansive in nature.

Integration increases in probability with a data hub incorporating ‘lateral thinking’, i.e., multiple brands, multiple shopping experiences over a wide range of different products and services into its pattern recognition. To be clear. Integration demands a mature, well experienced, behavioral data hub. That is why many basic algorithms or less-robust systems struggle. Many of them are forced into a more simplistic compromise scenario, sometimes in critical touchpoint vectors, which increase the likelihood of less-than-optimal end experiences.

• A.) Behavioral data accumulated by a manufacturer with regard to its own products/services and customers certainly have some ability to shadow a human in the shopping process but it will inevitably have some blind spots. The blind spots are created through normal bias. For example, a local retailer using their own data incrementally reduces their definitions of shopping and shoppers. This is also true of manufacturers. As noted in Human Algorithm, this creates a more linear” looser” algorithm and a more fragile shopping experience. The truth is this setup is focused on transactional value – a valuable objective, but may miss out on total value. While outside supplementary data can be purchased it lacks the organic iterative pattern recognition which a robust behavioral data hub can draw from. Inevitably this setup will lean into compromise more often than integration.
• B.) A robust behavioral data hub augments specific manufacturer data as well as customer data increasing the probability of each ‘part’ in the shopping experience is managed well and the ‘whole’ of the experience meets expectations as well as desires. The robustness of an experienced human algorithm knows when to expand the shopping and when to eliminate some randomness in order to maximize value experience. The structural lift is subtle but increase value in 2 ways: (1) eliminates shopping waste – time, energy, thinking, and, (2) increases probability individual shopper meets desired objective. Inevitably this setup is grounded in integrating shopper preferences, not compromising.

All that said. It is extremely difficult for a single manufacturer to replicate the value creation of a mature robust behavioral hub that spans beyond that single manufacturer. A manufacturer’s data, even when supplemented, is bounded by what they make. This is important because a manufacturer can also have billions of interaction data points in their knowledge hub, but not all billions of interaction points are created equal. We say that because, on the other hand, a behavioral hub which captures manufacturer, retailer and customer across a spectrum of manufacturers, retailers and customers brings a lateral thinking knowledge base to behavior that is simply just difficult for anyone to replicate – they may get close but not to the same level.

Which leads us back to experience and value.

Optimal value is created through the accumulation of value ‘moments’ from the entire experience – shopping to purchase to satisfaction with purchase. Any compromise at any step in the process translates into a less-than-optimal value experience through an increasingly more narrowly targeted path. “A” increases the likelihood of this. On the other hand, a robust behavioral data engine increases the probability of integrating nuanced knowledge, while bounding behavior in a positive direction, which increases the likelihood of meeting all – needs, wants, desires, hopes, functional, price – into the entire experience, therefore, increases transactional value because of a structural lift to the right moments in the shopping.

The value of an AI infrastructure, leaning on algorithms, is that knowledge tends to pool rather than trickle throughout. AI (a Human Algorithm) becomes the irrigation system for institutional knowledge, data, individual knowledge and resources. In fact. Algorithms are actually the order needed to provide the guardrails to an emergent shopping experience <which is basically customizing to each shopper in some form or fashion>.

Algorithm design for mass usage tends to flatten, called digital flattening, therefore, a Human Algorithm design, both predictive and emergent, is to encourage non-flattening or at least acknowledges the natural arc of flattening and create a counterweight to that flattening thereby enhancing the behavior tied to preference and tamp down the less-than-desired aspects. In doing so it provides a value lift to every transaction AND every brand the hub supports.

Which leads us to constantly increasing value.

Value is never a stagnant concept. Not only does value vary from person to person but it varies over time as experience is accumulated. The possibility of the ability to integrate shopper preferences spanning across an entire brand portfolio is an embedded value lift which organically, iteratively, increases in value almost everyday with the accumulation of additional shopper interactions. In other words, a behavioral hub leading the race in an industry will always lead as long as it is accumulating knowledge. Or, maybe said differently, a behavioral hub offers a story which incorporates a want and a need and a desire within every brand in the portfolio. Ponder.