The future of personalization
How MotivBase can automate the personalization experience for digital content and commerce.
Because his retailer generates recommendations based on an analysis of what hundreds of thousands of other consumers buy. This analysis of the what, helps the retailer identify that “most of the time” when a consumer buys eco-friendly disposable diapers, they also buy organic shampoo/wash, moisturizer, etc.
Nothing wrong with an approach like this, except it’s not truly personalizing John’s experience every time he interacts with the retailer. It’s certainly customizing it because the same solution is applied to numerous others who broadly behave like John, but it’s not achieving true personalization. Which is why when it came to picking a moisturizer for his infant, John’s retailer never suggested that he try coconut oil.
How can MotivBase transform John’s baby shopping experience?
With MotivBase, we’ve built an intelligent engine (IE) that can study any free text and identify the hidden meanings they communicate in culture — outputted in our app through thousands of permutations and combinations of four cultural factors — motivations, attitudes, values, and fears.
What our system considers free text can come in many shapes and sizes. It can come in the form of billions of consumer conversations across the social web. Certainly, this is what drives our application. It can also come in the form of product and category names and descriptions.
This is where MotivBase has the potential to fundamentally transform the personalization experience.
If we feed MotivBase with thousands of product skews from a retail database, our IE can create hundreds of thousands of personalized “personas” for every possible permutation and combination of those product skews. A persona here is nothing but a frame of meaning, made up of a permutation of the four cultural factors — attitudes, values, motivations, and fears.
So when John logs into his retail account and picks just one product to add to his cart, MotivBase immediately assigns him a “persona”. And based on this persona, matches him with hundreds of other products that he could also be interested in. Of course, this persona is a variable and constantly evolves as John chooses new products to add to his cart. The more he interacts with the system, the more nuanced his persona becomes, and the more nuanced the resulting recommendations become for him as well.
Let’s look at a simplified example to explain what we mean.
Let’s say John started his search by first looking at liners that could be used with cloth diapers. At this moment, as he’s just begun his experience on the retail platform, MotivBase would immediately assign him a persona made up of the following motivations and fears.
With this initial persona, our engine would already begin to identify patterns of products and recommendations that would be more aligned with John’s values, motivations, fears, and attitudes. For example, MotivBase would know that it really shouldn’t be recommending an infant formula solution to John at this moment because his values and motivations don’t align with those associated with infant formula.
Instead, MotivBase would be more likely to recommend a breast pump, as its associated values and motivations align more with the initial persona John was assigned.
Of course, what we’re showing you above is a highly simplified version of what actually will occur on the back end as MotivBase’s IE crunches through hundreds of thousands of product skews and consumer interactions. But the point is that the consumer’s personalized profile is an evolving one, and one that is always based on their values, motivations, fears and attitudes. Better yet, it is auto-populated as the user begins and continues his/her interaction with a platform. Which means, MotivBase is constantly bettering its understanding of why each consumer buys/does what they do, and uses this understanding to personalize and constantly evolve the user’s experience.
Different from existing recommendation engines.
Because MotivBase allows us to build an understanding of who John is — what his motivations, attitudes, values, and fears really are — it allows us to (in real time) understand why John buys what he buys and accordingly transforms and personalizes his content and buying experience.
The Scope of applying MotivBase’s IE to personalization.
MotivBase can be applied to personalize any digital experience where free text is involved. That means MotivBase can help brands and retailers personalize digital content and Ecommerce delivery, in real time, as their users engage and interact with their digital platforms.
We believe this signals a disruptive shift in the way personalization has been attempted and implemented in the past because for the first time, the process can be truly automated based on not just what people say or buy, but why. Imagine no longer having to develop a set of personas manually, or having to assign content rules against those personas, or even not having to waste hundreds of hours in arguments with co-workers about whether certain aspects of the personas are right or wrong. Finally, we have an opportunity to end all of those wasteful and low-value practices.
With MotivBase, organizations can:
Personalize their Ecommerce experience for each user from the get-go and maximize dollar spend per user.
Create an evolving definition of who a user truly is - one that evolves with a consumer's natural evolution in values. Which means you avoid moments where your customer could very easily have bought that new product from you, instead of buying it from your competitor (because they didn't think you offered such products).
Personalize the delivery of content, applications, and even features.
Importantly, cut down on time and dollars wasted in developing web personas and profile rules etc. for content delivery.
Let’s begin with a simple fact.
Personalization is most effective when we don’t just understand the patterns in what people buy, but rather go far enough to understand why they buy.
Because people buy based on shared values, not just based on needs.
Let’s take John Doe as an example.
John has a 5-month-old at home. He only buys fragrance-free, eco-friendly diapers and wipes. He buys natural shampoo and body wash made with botanical ingredients, but refuses to buy “natural” moisturizers and instead prefers to rely on coconut oil as a moisturizer and anti-bacterial for his infant.
He buys most of this online, with his favorite retailer. Yet, only about half of the recommendations, his retailer provides he finds truly relevant.