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

the archive of the forgotten

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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.

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“Once the inner connection is grasped, all theoretical belief in the permanent necessity of existing conditions collapses before their collapse in practice.”

Karl Marx: Letter to Ludwig Kugelmann (July 11, 1868)

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“Chance favors the connected mind.”

Steven Johnson

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The truth is in today’s shopping world a business, and its brands, maximize its value not through consistency, but coherence. This creates a somewhat tenuous inner connection of things wherein nothing can collapse; except within more of itself. What I mean by that is there is no one thing that creates the value, but a number of things linked which can shrink in on itself without nurturing. What this means is to nurture one must find ‘selective consistency’ (the structural value embedded within) and tie it to agility (the ability to be malleable to accommodate individual buyer preferences). This is where a Human Algorithm (or algorithms driven by a behavioral data lake) offers a unique opportunity. We often don’t think of a behavioral data hub or AI design as part of experiential value consistency, but we should. Often the core is not a shared strategy, but a shared engine.  And, yes, through that distinctive engine you can create a distinctive “shareable” brand asset. It was Mark Ritson who suggested every brand should have codes or distinctive assets which create links to customers of a brand. He mostly suggested so with consumer brands, but we would suggest it is even more important in B2B in that it creates mental availability AND value association. What it does is create simple Mental availability, grounded in value offered, as a key driver for success to customers.

Which leads us to Data has always been a tool, now it’s a brand asset.

Data has always been a competitive tool in B2B. CPG companies effectively used data for years to increase facings, gain shelf positioning, negotiate promoting in store and acquire orders with grocers & retailers. Car manufacturers did the same with dealers and, well, wander an aisle of any retail outlet and you will most likely find a manufacturer, or owner of some brands, who dominate that aisle simply by use of data. That said. For decades large businesses with multiple brands have struggled with how to embed the manufacturer brand value within each of the individual brands. In the past we would see “manufacturer coupon events” on an annual basis where companies like P&G or Nabisco or almost any CPG company would have huge ads with coupons for all the brands in their purview or maybe there would be an insert showcasing all the brands. The hope was that at least once a year each of the brands would get a one-time structural value lift through association. Technology has changed all that. Now data is the glue and the fact that all the brands within a CPG portfolio can combine all their shopping, and shopper, data in a behavioral hub permits each of the brands to gain some agility in the marketplace (through knowledge) and, yet, maintain some linkage to all the other brands. What technology did was hone the tool and what algorithms did was made it an efficacy weapon – targeted efficiency and effective shopping experience tool. A mature behavioral data hub wielded with a well-designed algorithm offered a holding company the ability to not only tie their brand portfolio together strategically, but also enabled an enhanced value structure to all brands. What we mean by enhanced value is a historically coherent data transaction accumulation created a solid foundation to apply learning from one product/service transaction journey to another – lateral, or adjacent, thinking in algorithm form. It stops stratifying behavior – siloed bounded behavior – and enables incremental iterative progress from one brand to another.

This is brand coherence and the data hub offers some structural value lift. It permits individual brands to scale tied to core value. The database becomes a multiplier advantage to each individual brand. Correspondingly, it becomes a multiplier in value to the distributor/seller as it offers coherent positive experiences, of value, across multiple brands.

Which leads us to the reality that businesses with a full portfolio of brands are always in a tug of war between “I” and “We.”

At the core of this tug of war is, strategically, the agility of a brand alone to capitalize in its market segment versus collective power of portfolio to offer some structural value. Philosophically, this is self-interest versus collective interest (and, yes, a brand within a portfolio pursuing its own self interest can do so to the detriment of the greater portfolio whole). This tug of war is common and natural in that brands inevitably deem distinction, and/or differentiation, as the key to maximizing value in a specific segment. This is true, yet, this tug of war runs the risk that the parts ‘grow’ to the detriment of the whole. The intent of any brand portfolio owner should be to find ways, or a way, to raise the value of the ‘whole’ so that all the parts rise in value WHILE permitting individual brand agility. This is structural value creation, but more importantly it means each brand/product can pursue self-interest (agility in niche), but the center holds offering value to all.

Which leads us to experience coherence through a Human Algorithm.

When we experience something in the physical world, we absorb a wealth of information in front of us to help build a picture and form an impression of what we perceive, taking in the sights, sounds, smells, tastes, and textures. A Human Algorithm needs to sense the preferences for all those things to create the optimal shopping experience. Often this is created through what someone ‘feels’ which is where a robust behavioral data hub offers its greatest value advantage in two ways:

• Transactional coherence.

Cadence. Every shopping experience has a cadence. A rhythm it seems to work to. Now. It isn’t just one cadence, but a coherence of cadences. What we mean by that is there are patterns embedded within patterns and each shopping experience has a subtly different cadence of its own. The effective AI design cadence accommodates each sub-cadence. It is like a dance similar to one of those huge ballroom things where partners get exchanged at different points in the dance. It isn’t always seamless but, yet, the dance (viewed from overhead) has a sense of coherence and cadence.

Anyway. Everyone just should accept the reality that every shopping experience has a cadence driven by the shopper, but also is a reflection of how the system works. It is, in essence, what we generally feel. The danger resides in the failure to acknowledge this ‘vibe’ is a complex mix of pacing and cadence dynamics – fast, easy, slow and thoughtful. Even a Porsche engine has slow moving, even solid, pieces in combination with pieces moving at rates almost invisible to the eye. To be clear. Cadence is difficult to ‘see’, but with a mature behavioral hub and well design AI interaction system cadence occurs. Cadence is latent value.

Gravity. Every shopping journey has gravity. What we mean by that is left to its own devices a shopper will end up on the ground. A great shopping experience is one in which the AI sees and senses the shopper gravity in order to (a) fly or (b) simply keep things from crashing, i.e., end up in a place where preferences & expectations are not optimal. Here is the tricky part. This center of gravity is good important because, in its conserving energy, it keeps all the expended energy from flying off into chaos, albeit it can also be bad important in that it sacrifices progress in doing. Gravity keeps the shopping experience grounded, but the danger resides in that the experience only has the feeling of speed and achievements and all the while it’s just one huge hamster wheel, i.e., the shopper is spinning their wheels getting nowhere to their desired outcome.

To be clear. Gravity is difficult to ‘sense’, but with a mature behavioral hub and well-designed AI interaction system gravity is effectively navigated. Gravity, well navigated, is latent value.

• Structural coherence.

Each of the things highlighted above point out the transactional experience, but what about shared experience among all brands when a behavioral hub undergirds all brands and all transactional journeys?

Well, going back to cadence, a shared behavioral hub has a cadence; a rhythm. That shared rhythm among all brands creates a subtle consistency offering coherence to all brands interacting with the hub. We would be remiss if we didn’t point out that this cadence expands out to the retailer and, over time, shoppers also gain a sense of “what it is like to interact with them” which is part of overall value and creates a subtle shared value experience throughout all brands.

As for gravity, successfully navigating gravity translates into shopping experience progress – which is often overlooked in the value equation when assessing ‘buy satisfaction.’ As noted upfront, people don’t buy experiences, they buy value, and the experience is simply an accumulation of value interactions. Once again, navigating this gravity expands out to a retailer as in “they didn’t rush me” or “they let me explore some of my preferences without wasting too much of my time” or any number of valued ‘customer respect’ touchpoint moments. It is a subtle shared value experience among all brands.

Which brings us to subtle little things translate into non-subtle larger experience.

The purpose of coherence should serve the purpose of the business – to benefit people. In benefiting people everything in the experience gains value. In other words, subtle, well applied, creates experience coherence which increases final transaction optimal value <if not highest value possible>. Human Algorithms subtly shadow people offering value behind the scenes, but that value outcome appears in real tangible achievements – higher sales, higher velocity of sales, higher satisfaction.

Which brings us to subtly improving everything.

The reason this depth of discussion over algorithms and AI is important is because in an algorithm driven business world everything has a goal in mind, but the consequences are often unseen. Business goals are usually innovations and sales/revenue/profit, but in the pursuit of those there are a variety of externalities created or affected that inevitably are not only of importance to the economic effects of AI but to societal effects. AI is inherently an iterative innovation itself and one that triggers cascades of complementary innovations. Each of those things have externality effects to, well, humans .. either by creating opportunities or closing doors on opportunities – both of which have real world consequences to the lives of humans. This entire paragraph is a rationale for why subtly improving everything is important – either thru intent or in reality. And because this piece is about coherence that is where we will end. Subtly improving everything maintains some inherent ‘goodness’ coherence which will fundamentally engage some sense of coherence for everyone and everything. In a world in which people are feeling increasingly uncertain and everything feels increasingly fragmented, this coherence action will prove invaluable not just to some business who embraces it, but to society as a whole.