Q&A with Absolutdata CEO on AI-powered decision making

Understanding how to use data and analytics for effective decision-making requires evolving as technology does — and this is not lost on California-based Absolutdata. Starting 17 years ago to create scalable, data-driven impacts, Absolutdata has adopted the use of AI and machine learning as a part of their solution.

We sat down with Anil Kaul, CEO of Absolutdata, to learn more about how they’re using AI to provide the very best for their clients.

ClickZ: Where did the idea for Absolutdata spring from?

Anil Kaul: Since beginning in 2001, our goal has always been to use data and analytics to create scalable impact for organizations through recommendations. Our focus used to be more on using data and analytics as a service that would provide insights for companies to make the right decisions.

We’re still doing that, but we’ve now added AI and machine learning into the mix. We’re combining AI with a technology platform to provide data-driven recommendations and solutions for sales and marketing teams.

Think of it like a GPS for decision-making. A GPS can tell you where you are, where you need to go, and which paths you should take to get there. That’s exactly what we’re doing for sales and marketing teams, and making the right recommendations is the most critical piece of that.

CZ: What sets Absolutdata apart from the other data-driven recommendation software solutions out there?

AK: There’s so much hype today around AI. Whether or not people and companies use it, they’re talking about it.

What sets us apart is that we’re actually using it. We took a step in that direction five years back, before everyone was talking about it. We had a problem and saw AI as the solution. What differentiates us is our experience and expertise and actually having a sophisticated AI solution that’s giving business owners real results.

There’s a huge amount of learning that comes along with making recommendations that can actually create impact for a business. We have some companies where the sales team needs recommendations every week, so our system creates for them weekly plans.

We have other companies that need to know what kind of marketing campaigns they should be running. For example, we might have a client that does frequent paid promotions. We have sophisticated AI models that can use real data to give that client an optimized paid promotion calendar. The client can use that calendar as a basis for planning promotions, adding and subtracting things where they see fit, but they have a solid starting point.

Where it would typically take a marketing team weeks to get to that point manually, it takes our platform all of 10 minutes. The data is there, the models are there, and the answer is there. We can give the client the full solution, making them more efficient and agile as a whole.

CZ: How does this look exactly on the client’s end?

AK: The client gets access to a tool that fits right in their browsers. They can log in and see the recommendations that the system has made based on the preferences and requirements they identify. Our goal is to provide a recommendation at the right place and the right time and to make it simple for the client to move ahead with it.

In that spirit, we integrate with many of the tools that our clients use already, whether it be a CRM platform or trade promotion management software system, etc. With a click of a button, making a decision and executing a recommendation happens with the tools they already have in place, making the whole process seamless and efficient.

On the client’s side, part of the process is their acceptance or rejection of recommendations. If you think about a large ecommerce site that makes recommendations for things you might want to buy, a consumer might only consider 30% of the recommendations to be something they’d actually purchase.

In that scenario, 30% is fine. In a business situation, the bar is much higher. Our clients can’t afford for 70% of their business recommendations to be bad. In an effort to get as close to 100% as possible, we ensure that the system itself is learning.

For example, if a client rejects a recommendation, the system learns from that. The next time it makes a recommendation, it considers the rejected recommendations alongside the success of the ones that were accepted, fine-tuning and improving all on its own.

CZ: What’s something that really excites you about Absolutdata?

AK: The emotional response we get from our clients is particularly exciting for me. I come from the analytics world, which is pretty straightforward, very cut and dry.

By providing clients with these recommendations for better decision-making, we’re able to stir up excitement. We’re able to give them tangible things that they can do to improve their businesses, and the excitement we hear back from them as a result is a very cool experience. Our tool gives our clients a sense of ownership over their business that many felt like they were lacking before.

Like I said before, we’re also very excited to be implementing technologies that other companies only dream about right now. We’ve been able to cut through the noise and the hype and actually use AI technology.

At the end of the day, I really love hearing how much our clients enjoy working with our tool. That brings me immense satisfaction.

CZ: I can imagine!

AK: Absolutely. We’re creating a new category of recommendation systems with AI and machine learning that actually provides tangible results and benefits for our clients.

That’s the exciting piece, and this is what we’re able to say in our sales calls. We can actually point to our system and point to successful recommendations and prove that we’re not making this up. This is where a lot of initial confidence from the client comes from.

Once they implement in themselves, they can see it working for themselves, and that is very cool for us.

Quick facts about Absolutdata

• Employees: 400
• Year Founded: 2001
• Headquarters: Alameda, California
• Clients include: Adidas, Uber, Godiva, Sprint, Kellogg’s, Levi’s, Autodesk, Kia
• Martech Landscape Category: Business/Customer Intelligence and Data Science