How Business Leaders Can Evaluate SaaS Product And B2B Data Vendors

President & Chief Operating Officer at Apollo; a leading provider of data intelligence and sales engagement platform. 

The sales funnel is collapsing. With growing information transparency in B2B, the sales process is rapidly shifting to the buyer. Buyers are increasingly managing the bulk of the sales process themselves before talking to a vendor. Hence, there is growing importance in marketing and buyer enablement. 

To make matters worse, buyers are faced with increased noise and confusion with vendor selection. Now that the price of tool building is at an all-time low, every SaaS category has upward of 40 different vendors, giving rise to buyers interacting with one another on review sites and social media.

This interaction creates a winner-takes-all dynamic as all the buyers try to convince themselves into buying the best of breed products. It also often causes a dramatic inflation of high-quality products. We see this in category after category where the dominant product takes a bulk of the market share while other SaaS vendors figure for scraps. 

This trend of good ideas getting overhyped at the cost of moderate ideas is not limited to SaaS vendors; we also see the same thing in financial markets where good assets are selling for huge premiums. 

If you’re a business leader looking at integrating SaaS products into your workflow, it’s important to understand how to weed through a market full of potential vendors. These are two of the most common approaches you have as a buyer when evaluating SaaS products and B2B data:

1. “Best of Breed” Vendor Selection

In the “Best of Breed” vendor approach, the goal is to buy from a single vendor that is best in its own niche. For example, you may purchase a marketing package from one vendor but purchase an accounting service from another. The “Best of Breed” vendor selection approach is all about finding the best offering for each application area.

There are many advantages in this approach: it’s quicker to decide with confidence given it's the most popular vendor in a category; better ROI; rapid implementation; its support of open integrations; and its ability to integrate with other best of breed solutions.

2. Multi-Vendor Approach

Next, is the Multi-Vendor Approach. It is particularly useful when you are outsourcing a complex task and want a couple of vendors to reduce risk or when you feel one vendor may not have all the skills to deliver and multiple vendors have a better chance to provide coverage. 

There are both advantages and disadvantages of the multi-vendor approach given the task at hand. With a multi-vendor approach, there is a risk that vendors’ solutions may not be compatible with one another. However, by having multiple contacts, you would eliminate the risk of being too dependent on a single contractor for a task. If there is fall through from a vendor, there is potential for another vendor to take over some additional components.

Which Is Best For B2B Data?

Data serves as a computational snapshot of an ever-changing world. No single data vendor can offer a complete picture of our reality. But, I believe that looking at multiple vendors can certainly give you a more comprehensive look at the world. Analyzing multiple vendors can also help increase confidence in the record quality if you see the same results from three out of four vendors. 

Now, if a company is buying a CRM solution, in my opinion, it will be most beneficial to buy the "Best of Breed," offering a single solution in a given area for the entire company. Going for the multi-vendor approach and integrating multiple CRMs would not make any sense. 

Keep in mind that a "Best of Breed'' strategy of vendor selection assumes that a dominant vendor can suffice for the defined use cases. As we established earlier, in B2B data, no one vendor has all the answers. Hence a multi-vendor approach can be more favorable for B2B data over a "Best of Breed" approach to vendor selection. 

So the question remains: When and how do you operationalize multiple data vendors?

How To Operationalize A Multi-Vendor Strategy For Data

Most data vendors provide real-time API to their datasets. Ideally, a buyer could integrate API with multiple vendors (not more than 3-4, after which will hit diminishing returns). The goal would be to create a cascading rules engine in the backend that calls different vendors based on the request, quality and cost to ensure the highest ROI. 

We’re seeing more and more buyers implementing this type of hybrid approach to make real-time data buying decisions. Below are a couple of approaches I have seen in such an implementation:

• Highest Confidence First

In this approach, buyers score all the vendors by confidence, possibly for data fields. For example, you could say Vendor A always has better phone numbers but Vendor B has the best revenues and employee size numbers. Based upon the specific task, one could call the right vendor to have the best result. 

• Least Cost First

This approach is slightly more optimized for ROI. If you find that different vendors offer the same record, then starting with the lowest-cost vendor often turns out to be the most optimized approach. To follow up, a second call can be made to the next lowest priced vendor to ensure the email address and phone numbers obtained earlier were the same. If a record is not available in the low-priced vendor, then buying it from the most expensive vendor makes sense. 

In this approach, as a rule of thumb, if the data is to be used lower in the funnel, a much higher unit price can be justified versus if the data is to be used at top-of-the-funnel campaigns, a minimal unit price serves well. 

Every data vendor will claim that their data is the best and, hence, one should pay more. But I believe that smart business leaders who are looking to buy B2B data are not locking themselves into a single vendor and are making real-time decisions into data purchases using a variety of rulesets and cascading APIs.

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