AI powered sales&marketing assistants can help you reach the right customers through the right channel with the right message.

We had a good discussion with Anil Kaul, Co-founder and CEO at Absolutdata. Having started Absolutdata back in 2001, he is one of the pioneers in big data, analytics and artificial intelligence. We discussed their primary areas of focus, their unique value proposition, cost and duration of deploying NAVIK, their core products. He explained in detail how AI powered sales & marketing assistants can help you reach the right customers through the right channel with the right message. Below you can find our podcast edited for clarity and brevity.


Anil:Let me give you a little background about what we are doing at Absolutdata and what role AI plays in it. So, unlike a lot of other companies that you would probably speak with who would be startups that are building AI products. We are actually a very different company. We were founded about 16 years ago in 2001 with the goal right from the day one was that we wanted to build tools, technologies and analytics to help companies make better decisions. That was our mission and we’re still very focused on that. However, when we started the company at that time there was not a lot of analytics being used as much. So, we actually focused on doing data analysis for companies using traditional statistical methods. That’s what we have done over the last 16 years. We have about 40+ customers now, about 350 to 400 people at this point of time. Headquartered in here, in the San Francisco Bay Area and at a big part of our team sits in India, even though our customer reach is fairly global. Our focus traditionally has been on the marketing and sales function of an organization. We also are funded by the private equity arm of Fidelity from Boston and in fact, we used a lot of that investment into building up our AI based products.


To give you a little history of how we came into AI and how we started using it. About three or three and a half years back I had this epiphany where I realized that the analytics that we had been doing, all the big data and everything that people talked about for quite a while wasn’t having the kind of impact on business results as it should be having. The gap that we realized was in how analytics were getting implemented. Typically analytics tended to be more like as services organization either within the company or working with companies outside. It was very good at providing insights. However, it was not doing a very good job of converting or translating those insights into actions the business teams should be taking. So, we decided to take a step in that direction and said we will integrate technology very deeply into what we do at Absolutdata and build solutions that not only provide insights based on the data but actually recommend actions to take. As we started working on that business problem, very quickly we realized that the traditional statistical methods which typically tend to be more focused on regression kind of methodologies are not the best methods to be used in a situation where you are going to be providing recommendations. So, that is when we started going and using machine learning. We started building with some deep learning techniques available at that time. But the need for using AI into what we’ll building actually came for us from the type of solution that we were building rather than saying AI is a cool technology and let’s use it.


So, what we have done is, we’ve built up a platform which is called Navik which means navigator in Hindi. There is a Navik component which targets the Sales function of a B2B organization and there is a component that targets the marketing function of a B2C organization. We also have a third piece in there which is focused on introduction of new products but let me focus more on the marketing and sales pieces. To give you a sense of what Navik can really do, maybe I can spend a couple of minutes just talking about our SalesAI product, Navik SalesAI as we call it. So, if you think of a salesperson, a salesperson today is in a situation where there is a lot of information that is provided to him/her. However, that information does not guide them to what they should be doing. There are some companies that are certainly providing some guidance but most of that guidance today is focused on prioritizing which particular leads a salesperson should focus on. However, once given a lead, the salesperson is supposed to figure out on his/her own what should they do about that lead. There might be some guidelines that sometimes are available but beyond that everything is left as the responsibility of the salesperson to figure it out. So, what we did was, we said let’s look at what is the day-to-day life of a salesperson and let’s build a platform which works like an assistant to the salesperson. So, what Navik SalesAI does is, it literally creates a weekly schedule for the salesperson and what I mean by weekly schedule is, it goes, looks at all the data that is available in the organization particularly in the CRM system and in the ERP system, it looks at any data that is available about outside information, for example one critical component that we bring in to Navik is the various news information that is coming out, announcements and so on, and based on that it does three things.


First, it identifies new opportunities. Opportunities that might not be included in the leads that they already have. That’s a very critical piece that it can actually come up with leads that are not part of the leads that you’ve been given. For example, It can look at your current set of customers based on their past buying behavior, predict what are they likely to buy now. So, it creates new fresh opportunities in the pipeline. It also then prioritize all the opportunities that are available to you for that particular week. So, it says for this particular week, here are the top ten opportunities that you should focus on. So, that’s the step number one.


The second step that it does is, for each of these opportunities, it creates the optimal path to closure. So, it identifies sales actions that the salesperson should take. So that they can close that opportunity in the fastest possible time. This information is informed by the past history that you might have of that particular account or of similar accounts in the CRM database. For example, you would know what activities actually correlate with a deal going through the pipeline faster at each of the pipeline stages. You might have information available from other accounts that are similar and most importantly you have information available from best sales people in the team, in the company. They’re doing certain sales actions and through AI and through our models we are able to learn from that and recommend what will be the best closure path for this particular account.


Cem: Could you provide a few examples to these actions on the path to closure?


Anil: Yeah! So, actions on the path to closure are things like setup a meeting,  invite somebody to a webinar, meet additional people. So, in this organization you need to connect with four people to close this deal and here are the four people that you should be connecting with. It optimizes and figures out what is the best sequence of connecting with these people. Should you start your conversation with person A or person B and when should you bring other people in the conversation. It looks at all this information and essentially puts together the path that we are able to discern from the data so, that account can be closed by a certain date.


Cem: That’s quite interesting indeed. You are using only that company’s data to estimate this, right?


Anil:That’s correct. We just use any data that they have.


Cem: Because what could also be interesting is, if you had multiple companies that are interacting with a possible customer but about different products then it could be, I don’t know if this would be possible to anonymize and so on but if you could pull together those pieces of data and build an understanding of the company, that would be interesting, right?


Anil: Absolutely. you know, I hope we can do that someday. The challenge that we’re facing right now is about being able to pull out all of that data together and be able to leverage that data for that company but yes, that is absolutely a possibility and it would make the recommendations significantly more better but at the same time the I think the good news is that even within the data that you have, just using the data at typically companies have, you get very significant improvements in closure rates through this. So there’s a lot of learning already sitting in the data. Of course if we were able to combine outside data, other interactions, then that would become that much more stronger and better. So, that was our second piece which is for every account: Tell me how do I close this account.


Then we have third piece which is every contact that you have with the account, we recommend what that engagement should look like. If you are going to be doing an email what should the message say, what product should you be talking about in this email. It also recommends whether you should do an email or an in-person meeting. There’s lot of other information that sometimes companies have, sometimes don’t. For example one of our clients also provides guidance about if they have a contract with that particular customer, how much of the contract has been used. So, there’s a lot of information that is provided to the salesperson so that they can make that contact in the best possible way. The salesperson still has to go and do it. They still have to have that conversation but we will provide, for example a cheat sheet of what to talk about in that conversation. In one of the situations, we also recommend what is the most likely competitor they might be considering for this product and how do you counter that and what information do you provide. So, the whole idea is that we want to make the sales people successful in what they are selling and for that, provide them guidance on through the whole process of what actions to take, what is the sequence of actions that you should be taking and where and which particular account should be focusing on doing this. So that’s essentially at the end of the day, the core of what SalesAI does. It is not just a machine learning system that just tells you what you do. What we have also done is built a lot of interactivity in there which is that what we’ve done is we looked at how a salesperson plans their week and in the tool, we provided them ability to actually plan their week because just providing recommendations is not enough. This is a tool that a salesperson can use to plan their entire week. So, they can add opportunities on their own. They can take some of the recommendations and say I will not act on it this week, I will do it next week. They can invalidate a recommendation and every time you are doing this our algorithms in the back are learning that and so that, next time we are making those recommendations, it will take this learning into consideration. So that’s what our

SalesAI platform is.


The other type of platform is MarketingAI platform and I will do a fairly quick version of that. The MarketingAI platform has a very similar thinking as we have over SalesAI platform, except it recommends what campaigns you should be running in a particular week. So, marketing companies that do a lot of campaigns on Facebook, email and others have a very traditional model of doing campaigns. The traditional model of green campaigns is, I come up with a campaign and I find out what’s the best set of customers I should target with that. What we do is, we conceptually look at each customer and say what is the best campaign for this particular customer to run and aggregate from that to create the campaign’s that you should be running. So, we would be, for example at each customer level, predicting what is the next product they’re going to buy and when are they likely to buy that product and how would that get impacted by a promotion you are running. So, we then find the best combination of promotion that will get the customer to buy that particular product. It uses mostly the data that a company already has on their customers and is able to provide recommendations pretty strongly.


Just to give you a sense of impact on the SalesAI, we have done it really tight A/B test with one of our customers, it’s a  10 billion dollar company out of Chicago in the B2B space. We were able to give them a four percent increase in sales in seven weeks of testing. This is for a company that for the last three years has been flat or negative in terms of sales. So, that’s the impact that we’ve been able to create there and again, a really clean A/B test.


On the MarketingAI side we’ve done a pilot test, A/B test with one of the largest hotel companies in the US. There, our recommendations produced a %51 of the increase in revenue over their traditional methodology. So that’s the power of what you have there in the AI. The goal that we have for our platform is, build a platform to become like your AI assistant for what what you should be doing at your job. That’s what we’re trying to build.


Cem: That’s pretty impressive and it’s also pretty quick to set up I guess, thanks to integrations, right? Because in seven weeks you were able to get pretty solid test results.


Anil: Yes. Again, the both the platforms have been built with a view of quick integration with existing systems. So we’re not trying to replace any of the existing systems the companies have. Because most of the existing systems and existing technology tends to focus on execution. So, on the marketing side, you have a lot of CRM systems which are very good at executing. What we do is, we sit on top of that and make recommendations on which campaigns to run or on the sales side, what the salesperson should be doing. So, we work with for example Salesforce, Microsoft Dynamics, and integrate very easily. So, we built those integrations. The other thing that we’ve also done is, our analytic systems in the back, are also built in a manner that, once you bring in the data, it literally is a three-day setup for us. So within three days everything is set up and you can get up to speed and running. Again, typical setup doesn’t take only three days because there is a time involving getting access to the data, mapping the data to our system. So, it only takes about three to five weeks to do the setup but at the same time the whole goal behind all of this is being efficient in how we do that. In fact, we actually use AI even in our data mapping. This is part of the philosophy that we have at Absolutdata which is that we want to become an AI first company which means that we’re not going to use AI only for our products, we use AI in everything we do. Even, when I’m talking to our HR team. I am looking at opportunities how to make on HR decision-making better and faster through AI. Is there a tool that we can build that can do something for our HR team. We actually have a lot of small things that we’re looking at throughout the entire company and trying to build AI into every little aspect of what we do and make this very core part of our DNA. So, that’s a very exciting journey and I’m actually very much looking forward to just not having AI on the product side but also on how we’re doing things internally.


Cem: Yes. it’s quite exciting indeed. Finally, could you talk a bit briefly about what you can on the pricing aspect?


Anil: Our SalesAI product is priced like as a SaaS product so, it’s based on per seat. The pricing depends on the number of seats you’re assigning, so tends to be significantly less than what clients typically pay for their CRM license. It’s usually half of that. On the marketing side the pricing is based on the number of campaigns that you run or through our platform and again, the pricing for a campaign is between 4000$ a campaign to 8000$ a campaign. But again, all of the pricing we’ve done in a manner where you can test it and start running it without making a big investment and without having to make a big commitment up front. The whole goal is, you use it, you see the value and as you see the value you scale up the usage. Then you start paying more as you’re scaling up.

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