Delivering for Best-in-Class Wholesaler-Distributors
August 20, 2020  |  ByBenj Cohen, Entrepreneur of Artificial Intelligence Technology and Third-generation Distributor

As distributors look for new ways to connect with customers and boost profitability, they must consider all sorts of three-letter tools: ERPs (enterprise resource planning), CRMs (customer relationship management), MDMs (master data management), MRMs (marketing resource management), and so on. Unfortunately, many of these “solutions” end up causing more problems than they solve.

Enter the Customer Data Platform (CDP): A tool that truly solves customer engagement issues by combining analytics and action. Given the list above, I understand why distributors are suspicious of miracle tech solutions. However, let me explain what CDPs are, why they are fundamentally different from other tech solutions and how distributors can use them to leverage their most valuable resource, customer data, to drive huge growth.

What is a CDP?

As the name suggests, the foundation of a CDP is customer data. The basic goal of a CDP is to create an interconnected company infrastructure that allows businesses to use customer information to guide profitable action. This means that a CDP doesn’t just collect, consolidate and analyze data, it also directly generates sales.

Other systems like CRMs and MRMs may help inform action, but they do not actually perform actions. This means that the work these systems do is not directly valuable for distributors. A CDP, on the other hand, is designed to move past analytics into action, to directly drive value for distributors and their customers.

At Proton, we like to explain CDPs by breaking them down into three interrelated phases: connection, analyzation and activation. As distributors use CDPs to cycle through these phases, they can understand customer behavior and take profitable action like never before. Here is how CDPs work.

Connect

A CDP uses preexisting data to help create a complete view of customers. This can include things like customer purchase histories, annual spending, interactions with reps and locations. Or, in more advanced cases, this data can include intent behavior, such as emails that customers have clicked on, items they have viewed online or interests they have expressed to reps.

The more data you have, the better your data foundation will be. However, the point of a good data foundation is not just to compile data for the sake of data, but to start turning that data into revenue as soon as possible.

Analyze

Decision-making means mining your data to find actionable insights. This is probably the most technically sophisticated aspect of a CDP, as the systems must use advanced AI to do this.

There are many different kinds of data and analysis that can be useful. A simple example of this would be using e-commerce behavior to infer intent. For example, if a customer abandons a product in a shopping cart, you can infer that that customer is likely interested in that product.

On the more complicated side, a CDP could use advanced AI models to group customers into micro-segments based upon behavior. This way, if you know that two customers are similar, you may use interactions with one customer to infer what the other might be interested in. Of course with a CDP all of these processes are automated, so you don’t have to work through the data yourself. You can just sit back and let the CDP pair the right customers with the right products.

Activate

Once your CDP has made key decisions about what to pitch to whom, it can move on to the necessary task of figuring out how to execute those decisions. For distributors who interact with customers over multiple channels like email, e-commerce, reps and stores, this can actually be quite the conundrum.

For example, customers who have abandoned items in their online shopping cart may be interested in those products, but unwilling to buy them online because they have some questions. In that case, the best thing to do would probably be to have a rep talk about the product with the customer, instead of simply advertising the product on the website.

Without a CDP, employees must use different tech systems to try to manually coordinate this kind of information. However, because CDPs are truly all-in-one platforms, they are designed to handle this task. This means that CDPs can automatically inform reps about different items they should pitch, personalize a customer’s online shopping experience with offers that are relevant to them or customize different marketing emails to feature products that are most relevant to each recipient.

What is even more exciting about this phase is that it also creates an opportunity for more data collection. As customers react to the product pitches that a CDP has curated, they also generate more data about themselves. If customers buy items, you understand more about their purchase behaviors. Or, if they skip over a web-banner displaying a different product, then you learn that they are not interested. CDPs automatically collect all of these different kinds of data. This means that CDPs get continually better over time and run customers through continuous iterations of data collection, decision-making, message design and message distribution. As this virtuous cycle continues, companies can rack up an estimated 10–20% increase in revenue, according to McKinsey.

Conclusion

CDPs are an extremely effective tool for businesses because they bundle multiple complex processes into the same solution. CDPs not only give distributors a way to generate and collect customer data, but they also allow distributors to distill this information into smart actions and automatically execute those actions.

While there are many different CDP companies out there, distributors must understand that their businesses are unique. Unlike most B2C sellers, or even B2B sellers, distributors interact with customers over a huge range of channels. Unless a CDP is explicitly designed to analyze and coordinate behavior across reps, websites, marketing departments and stores, it will not deliver the kind of double-digit growth that distribution-specific designs can.

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Benj Cohen, Entrepreneur of Artificial Intelligence Technology and Third-generation Distributor

Benj Cohen, Entrepreneur of Artificial Intelligence Technology and Third-generation Distributor

Benj Cohen founded Proton.ai to help distributors harness cutting-edge artificial intelligence and dominate the digital age. He learned about distribution firsthand at Benco Dental, a business started by his great grandfather. While Benj was studying Applied Math and Data Science at Harvard University, he saw an opportunity to apply innovative technology to distribution, enabling distributors to sell more in modern markets.
Benj Cohen, Entrepreneur of Artificial Intelligence Technology and Third-generation Distributor

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