How to market AI products to internal customers?

Theoretically that should be easy ... if the #AI solution is developed to address a well-defined business need, with distinctly identified internal or external customers, and clear expectations about scope, development time, and resources needed to develop the product. But that's the theory.

In practice, there are different customers to be addressed, even internally, such as who will decide about the resources to develop the product and who will use it. This creates separate challenges to convince the buyers to buy it and users to use it. And depending on the users' data and AI mindset, additional investments may be required to strengthen the data and AI mindset among users.

Here is where the #marketing framework of #5Ps of marketing can help. This concept addresses the five components of typical product marketing: product, price, placement, promotion, and people.

Marketing is, by the way, the activity to define, promote, and sell products or services. The concept of a marketing mix to accomplish this was popularized in the 1950s to help companies in "developing the ‘right’ product and making it available at the ‘right’ place with the ‘right’ promotion and at the ‘right’ price, to satisfy target consumers and still meet the objectives of the business". This led to the concept of the 4 Ps of marketing. It was later expanded to include people to address, among others, experiences of customers.

Marketing AI products internally requires the same five components where the fifth element addresses the people using data & AI products. All these components are intertwined and together address the following questions:

1. Product: What business problem is the product addressing and how will it work?
2. Pricing: What will it take (money, resources) to make this product a success?
3. Place: Who needs to be convinced to provide resources to develop it and who to use it?
4. Promotion: How do you convince buyers to buy it and users to use it?
5. People: Is there a fundamental need to improve the data & AI mindset among users?

Products vs. projects

Before continuing, let me clarify what I call a product. First, a product, unlike a project, takes into account how the solution will evolve over time, from cradle to grave, leading to a product roadmap. In addition, while developing the solution you often realize all potential expansions that are not possible within the initial timeframe given to develop the first version. You then end up with a list of future features the product can be expanded with. And second, instead of throwing the project over the proverbial wall and hoping that the results are used, a product approach requires marketing the product to its customers and ensuring the product is used.

And why “AI products"? Well, I’m fully embracing here the AI hype … guilty as charged. Although I'm focusing here on AI solutions, such as recommender systems, simple analytics solutions such as dashboards will benefit from the 5 Ps framework as well. The key focus here is on solutions developed by technical people for business people. Other data products such as customer data platforms, for example, which are developed by technical people for technical people who use the same language, may not require the full 5 Ps framework.

1st P: #Product

The first P stands for product. Here marketers need to define the product or service and its unique value proposition. They need to identify the target customers for this product, which then leads to the must-have product features relevant for these customers. And they need to plan how the product will evolve over time during its life cycle.

From an AI product perspective, the first step is to understand what the business problem is or what business hypothesis exists that could potentially be addressed with an AI solution. The value proposition needs to define how this AI product will solve the business needs and what features it needs to accomplish that. And since time is always a constraint and not all features can be implemented from the start, a roadmap for the product evolution needs to be planned.

When thinking about #customers, you may realize that you need to distinguish between those who provide the resources to develop the AI solutions (buyers) and those who will use it (users). Let's take, as an example, a next best action solution that provides recommendation to sales reps in a B2B setting. The funding to develop or buy the next best action solution will be provided by the executive team, who will need to understand the value proposition and the benefits of this solution. However, the recommendations provided by the next best action system will need to be implemented by the sales reps. So, while defining the unique value proposition for the buyers (executives), you also need to define the product features relevant for the users (sales reps).

2nd P: #Pricing

The second P stands for pricing. Here marketers need to evaluate how much customers are willing to pay for the product or service. Since the price conveys the value of the product, they need to identify a price that will resonate with target customers. They need to analyze prices of competing products and position their product against competition. And obviously the price needs to be high enough to make the product profitable.

From an AI product perspective, you need to think of resources and time as the price of the product. The resources required are not only the analytics and technical resources to develop the product such as data engineers, data scientists, ML engineers, etc. Plan also for resources to launch the product and embed it in daily business decisions where change management coaches as well as support from the executive team may be required. And time is not only how long it will take it to develop, test, and launch the product, but also how long it will take to reach acceptable usage levels.

As for #competition, often executives are approached and distracted by software vendors with new "shiny objects" that are supposed to offer the best AI solution ever. These external solutions end up being competitors to internally developed AI products. It then helps to position internal AI solutions as not black boxes, explainable, easily expandable, usually less costly, and building intellectual property within the organization.

3rd P: #Place

The third P stands for place. Here marketers need to evaluate where they can reach their target customers. And they need to understand what distribution channels are most effective to reach the target market. This of course depends on the product as well as pricing. For example, you would not want to place an expensive electronics gadget at a grocery store. Or sell a can of soup at a jeweler.

From an AI product perspective, it makes here again sense to distinguish the two internal customer groups: those who need to be convinced to provide resources to develop the AI solution (buyers) and those who need to be convinced to use it (users).

The place to reach these two different customer groups may differ. Since both are internal stakeholders, you can probably reach both through informal 1-on-1 coffee chats. For executives (buyers) though, the best place to reach them may be at more formal presentations to individual executives or at their regular team meetings. To reach users you may have more options such as informal data “office hours”, brown bag lunch presentations, internal social media, or workshops with dedicated user groups to understand their needs.

4th P: #Promotion

The fourth P stands for promotion. Here marketers need to identify how to convince customers about their product. Why is their product better than competitors' product? And "better" in which dimension? Marketers then need to assess what promotion channels are the most effective to reach their target audience.

From an AI product perspective, especially here it is clearly needed to distinguish the two internal customer groups: those who need to be convinced to "buy" it (usually executives providing resources) and those who need to be convinced to use it (users). Convincing buyers (executives) to provide resources requires a sales pitch describing ideally financial benefits such as higher revenues or lower costs expected from the product. Convincing users to use it may work with numbers and facts, but here you need to keep in mind that change management is crucial.

Let's take here again the example of a next best action solution that provides recommendation to sales reps in a B2B setting. This recommendation system requires that sales reps modify their regular routines. You need to convince them what is in it for them through promotional videos, formal training, informal “open hours”, user chat groups, internal social media posts, and other means.

Both customer groups, buyer and users, can be convinced best by creating and telling a compelling #DataStory, one for each customer group obviously.

5th P: #People

The fifth and final P stands for people. It was added to the original concept of 4 Ps of marketing to address, among others, experiences of customers. Here marketers need to be aware of customers’ experiences with their product. Is the product intuitive to use? Is there a fundamental need to explain the product and its benefits to customers? This is in particular relevant for new product categories that customers haven't been familiar with before.

From an AI product perspective, this fifth P boils down to data & AI mindset of users. Data & AI may be intimidating to users and some fear that “AI is going to replace me”. This drives the need for #DataLiteracy programs to improve the ability to read, work with, analyze, and argue with data among non-data employees. At the end, it's not AI that is going to replace people, but people who use AI. These programs cannot be top-down corporate trainings but need to be developed bottom-up as data & AI evangelism, addressing the current state of the organization and explaining to everybody what’s in it for me.

Summary

Marketing AI products to internal customers should be easy, but in practice there is a lot of misunderstanding by all stakeholders: those who develop AI solutions, those who provide resources to develop them, and those who use them.

The marketing framework of 5Ps of marketing can help. This concept addresses the five components of typical product marketing: product, pricing, place, promotion, and people. It can be used to market data & AI products by answering the following questions:

1. Product: What business problem is the product addressing and how will it work?
2. Pricing: What will it take (money, resources) to make this product a success?
3. Place: Who needs to be convinced to provide resources to develop it and who to use it?
4. Promotion: How do you convince buyers to buy it and users to use it?
5. People: Is there a fundamental need to improve the data & AI mindset among users?

Do you find this useful and applicable for your organization? Would you like to brainstorm more ideas about this topic? Or would you like to get a free PDF version of this article in English or German? Just ping me.