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3 Ways Product Data Can Help Fulfill Your Brand Promise

Digital transformation as a concept has lost a lot of its meaning and has become a catch-all for many types of information initiatives.  What we need to remember is that a digital transformation is about rethinking not only the end-to-end user experience and customer journey, but the entire value chain and the fundamental reason why customers deal with your organization. If customers can get products cheaper on Amazon, does that mean you are destined to go out of business?  Of course not. Customer loyalty depends on much more than price alone. 

Loyalty is embodied in what your organization means to your customers – the expectations around quality, service, and selection as well as the intangible character that your organization represents.  These components include the many emotional elements that are part of that relationship, a relationship that can become much more nuanced than many people realize.  In fact, customer relationships are similar in many respects to those between individuals, reflecting the dynamics seen in interpersonal settings.  

A Harvard Business Review article[1] described customer relationships in terms of being intimate (does the brand understand the details of the customers’ needs and wants?) versus   transactional (a simple interaction entailing an exchange of money for a product). They can also resemble sibling relationships, buddy relationships, and power relationships.  Some interactions are analogous to having a one-night stand and some are partnerships or even marriages. 

The piece is amusing in its characterizations, but also very enlightening when put into the context of the hyper-competitive online marketplace. Is the Amazon brand your best friend?  Perhaps it is merely transactional. Perhaps your brand is your customer’s trusted advisor – always there to solve your problem with the best and most cost-effective solution. Always ready to share their knowledge and expertise and to help you first, before doing business – to make sure you are always satisfied.  

At the core of the brand is the sum total of expectations, emotions, and tangible and intangible value that represents how customers view your organization. Those expectations are, in effect, a promise. The way that promise is fulfilled determines how customers perceive the value of the brand. Meet those expectations and customers will be happy and likely come back (assuming they want what the brand represents to them).

Regardless of the nuances of the brand’s promise and the relationship of the organization with the customer, the details of relationship interactions depend on three things:

1. An ability to read and understand the customer.

This can be accomplished in many ways, from understanding segments and personas to reading their electronic body language as they interact.  Who are they and what do they want right now?  This understanding includes insight into what kind of relationship the customer is looking for.  If the customer just wants to get the cheapest price and move on, there is no point in asking for detailed information unrelated to the task at hand.  Don’t present things that are not directly related to the customer’s objective.

2. An ability to respond to the customer in real time.

If your company supplies industrial products and your customer searches for “mold stripping,” you can, by segmenting construction customers from plastics manufacturers, differentiate between a customer who wants to remove mold and mildew from a home and one who needs to perform routine maintenance on a plastic injection mold. The better your company knows its customers and the problems they need to solve, the more proficient it will become in interpreting the signals they are providing through their interactions and to present the correct information or outcome to each customer segment.

3. An operational ability to drive the machinery of content and data operations.  

Content and data operations need to be aligned with merchandizers’ knowledge of products, markets and customers, along with their problems and the solutions to those problems.  They also need to be aligned with the knowledge and expertise that marketing has about the lifecycle, buying motives, value expectations, and seasonal needs of the customer and marketplace.

These three considerations require feedback mechanisms for continual learning and course corrections as customer needs change and new products come to market, along with decision making structures to act on insights from feedback loops.  Successfully meeting each of the three goals also requires the ability to model customers in more complex ways than marketers and merchandizers are accustomed to.  And above all, it requires content models, rich media assets, and product data that contain the correct descriptors. This information allows products to be searched, retrieved, grouped, assembled, clustered and presented in ways that suit the customer, solve the problems, and fulfill the expectations they have of the organization and the brand.  

I was speaking with a customer recently who is working on a digital transformation project. He understood that product and content data and architecture were important elements of the initiative, but was having difficulty getting the organization to prioritize on these crucial work streams. Business leaders sometimes think of data and content as “givens” – that IT will address after the business sets their direction or as pieces of the puzzle that fall into place toward the end of the initiative when technologies are deployed.  This is a risky assumption. 

Product, content and customer models have to be defined as the business is reorganizing and reconfiguring the value proposition and value chain and redefining the digital representation of the brand promise. These iterative stages are at the heart of transformations.

Since it is such a significant part of the puzzle, waiting too far into the process to address product data will lead to project delays, cost over runs and a failure to meet customer expectations.  Product data is not a technical challenge – it is a business and operational one.  What processes need to be redesigned or redefined to feed the digital machinery of the enterprise?  How can “acts of heroics” that typically take place during product launches become a seamless and optimized part of the machine?  How will product attributes allow for retrieval of products and solutions in the endless combinations that will serve customer segments throughout their journey and in all the contexts that serve their continually changing needs?  These are not trivial questions and not “givens” that the IT organization or technology vendor can provide without planning and engagement with the business from the start of the transformation. 

Another misconception is part of the term “transformation” itself.  The word brings to mind an event with a beginning and end.  But digital transformations are ongoing processes and entail continued evolution and adaptation of the organization.   Whether or not product data technologies are going to be deployed as part of the transformation, the product data models and processes need to be considered from the start.  Getting a shared vision of a future state requires socialization across business units and departments.  Implementing changes to support the new digital machinery requires culture change that always takes longer than expected.  The point is to begin examining these processes from the start of your initiative—don’t wait for a tool or technology deployment.  By that time, it will be too late to build the process machinery and cultural changes that are needed to fulfill your brand promise. 

To learn more about fulfilling the brand promise and the role of product data and content, check out our Executive Roundtable on Brand Choreography: How Product Information Supports Your Brand Promise.

Does your product data support your brand promise? Contact us to learn more about how we can help see to it that it does.

[1] https://hbr.org/2014/07/unlock-the-mysteries-of-your-customer-relationships

 
Seth Earley
Seth Earley
Seth Earley is the Founder & CEO of Earley Information Science and the author of the award winning book The AI-Powered Enterprise: Harness the Power of Ontologies to Make Your Business Smarter, Faster, and More Profitable. An expert with 20+ years experience in Knowledge Strategy, Data and Information Architecture, Search-based Applications and Information Findability solutions. He has worked with a diverse roster of Fortune 1000 companies helping them to achieve higher levels of operating performance.

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