Is Identity Resolution the New, Must-Have Martech Solution?

There’s a bit of growing confusion and buzz in the martech space around the topic of identity resolution. It’s the new elixir being pitched as the critical additive to make your marketing technology stack work better, faster, and deliver better results. But is it?

There’s a bit of growing confusion and buzz in the martech space around the topic of identity resolution. It’s the new elixir being pitched as the critical additive to make your marketing technology stack work better, faster, and deliver better results. But is it?

For those of you familiar with the marketing technology space, every new solution comes with a blend of real value, hyperbole and needless complexity. Identity resolution is no different. Here I will try to unpack this relatively “new” capability and put it into perspective for marketing leaders. (Why did I put new in quotes? Keep reading to find out.)

What is Identity Resolution?

Identity resolution uses artificial intelligence (AI) to connect customer interactions and achieve a single customer view. The concept of capturing all customer interactions (marketing, engagements, sales, post sales), at the individual level, has been around for many years. However, achieving this goal has been very hard.

The reason is that customers interact with your brand across multiple channels (online and offline) while using multiple devices. Additionally, some interactions are anonymous or only provide limited identifiers. This interaction variability results in very complicated, disjointed customer data.

Until recently, most efforts at achieving a single customer view involved creating rules engines by which each interaction could be matched with other interactions and assigned to a single customer. Due to differences in the technology stack, channels employed, and the customer experience, rules engines had to be custom-built for each organization. This was expensive; enter AI.

Identity resolution uses AI in generating matching logic vs. using a team of analysts. The basic idea is to train the AI algorithm using known matches and then validate future correct matches the algorithm makes. This is why I refer to it as a “new” capability. In reality, it is only new because rules engines have been replaced by AI. For most marketers this change is only relevant if the match rates are better and the solution is cheaper than existing efforts are at achieving a Single Customer View.

What’s the Hype and Confusion About Identity Resolution?

While the addition of AI is innovative, it does not always translate into better match rates. Other major challenges with single customer view, such as the accurate collection of relevant data, still remain. AI, like any other analytic solution, also suffers from bad data and can put out spurious results. Therefore, verifying and validating AI matches is a task in and of itself.

The next issue to keep in mind is that identity resolution is probably not going to be sold as a separate solution in the near future. Within a short period of time, it will be integrated into larger martech solutions such as CRM or marketing clouds. Waiting to implement identity resolution could mean leaving the difficult task of systems integration to the cloud solution providers. However, the trade-off will be losing first mover advantage.

What Is the Value?

Single customer view has been the holy grail in marketing for good reason. With it, marketers can better understand the impact of interactions across the full customer experience life cycle. As an added benefit, marketers could also generate data-driven justifications for modifying or redesigning large segments of the customer experience. This will result in significant growth opportunities for your brand.

Despite the hype and confusion, identity resolution presents a great opportunity to finally achieve a single customer view. In theory, the introduction of AI should make identity resolution a desirable solution with better match rates and lowered costs. This means the evaluation of identity resolution tech is somewhat straight forward (though not necessarily easy).

The core evaluation question becomes, “Is the identity resolution solution cheaper and better at creating a single customer view vs. current efforts?”