Kuala Lumpur-based Predictry lands $230K to help mid-size ecommerce sites utilize advanced predictive recommendations
A Q&A with Predictry co-founder and CEO ST Chua. The Kuala Lumpur-based startup, which offers a predictive analytics and recommendations engine for online merchants, earlier this week announced $230,000 in Seed funding from the Malaysian Technology Development Corporation and Frankel Commercialization Fund. It was founded last year by Chua and Kevin Gorges.
SUB: Please describe Predictry and your primary innovation.
Chua: Predictry provides customized predictive analytics and recommendation engine solutions to meet the latest business needs of ecommerce, marketplace, web-listing, or content sites with the goal of assisting clients to increase relevancy, user engagement, and/or click-through rates.
Innovation for us is focused on a higher accuracy and relevancy algorithm, and also innovation through the business approach of understanding the needs of each business. Every client is of equal importance to us.
SUB: Who are your target markets and users?
Chua: Mid-size ecommerce sites that require customization for recommendations. A recommendation for consumer electronics is not the same as recommending food online.
SUB: Who do you consider to be your competition, and what differentiates Predictry from the competition?
Chua: There are two extremes we consider as competition. Firstly, the in-house developers. For big companies, they can afford to hire their own team of data scientists to develop customized solutions, though we always tell them that this is our bread-and-butter, and by allowing us to assist them they can better concentrate their resources to other parts of the business.
Secondly, generic plugins available on various ecommerce platforms. For small-and mid-size commerce site owners, it’s really hard to tell which plugin is good, and generally they go for the cheapest—which is not always the best solution.
What differentiates us is we customize our core algorithm to each business while making it a ‘no-hassle’ recommendation engine for our users and clients—meaning they don’t need to do much technically on their end to implement this except for inserting scripts.
SUB: You just announced that you’ve raised $230,000 in Seed funding. Why was this a particularly good time to raise outside funding?
Chua: We first performed market validation, and after obtaining serious interest from various parties, we now require funding to finance the development and deployment of a full commercial solution.
SUB: How do you plan to use the funds?
Chua: Seventy percent development, 30 percent commercialization.
SUB: Do you have plans to seek additional funding in the near future?
Chua: This is highly dependent on the interests we are getting from the market, but indications are yes; probably three-to-six months down the road to increase our speed-to-market and probably do R&D on parallel initiatives in this area of predictive analytics for commerce.
Only problem with a small team is that funding takes away your attention from the business at hand. So it’s a two-edged sword. But if there’s any investors out there who would like to go straight to the point and known for concluding deals fast, happy to explore.
SUB: What was the inspiration behind the idea for Predictry? Was there an ‘aha’ moment, or was the idea more gradual in developing?
Chua: I am a huge Amazon user, and about 40 percent of the books I buy on my Kindle are from Amazon. If that works for me, I am sure a good recommendation engine will help other sites as well. In my search for plugins, I realized most of them are not sophisticated, flexible, robust enough, and generally quite ‘dumb’ by relying on tagging or hard-linking—which basically means it is as smart as the person uploading the content; whereas those customized ones charge you an arm-and-leg for it. Therefore, we decided to go for the gap in-between.
SUB: What were the first steps you took in establishing the company?
Chua: Firstly, we identified and confirmed the need by talking to a couple of ecommerce companies from various sectors of online commerce. We then chose one to try on our MVP after getting a solid letter of intent, and we proceeded from there. Predictry uses a lean methodology to go-to-market.
SUB: How did you come up with the name? What is the story or meaning behind it?
Chua: I have to admit, getting a dot com name is a big challenge these days, so it took us a while to get this name, but we wanted something that could immediately give the idea to our customers. The key word here we decided to use is ‘predict,’ as ultimately we are predicting user behavior, sentiments, their needs, and likelihood to buy/sell/consume. So, we played with different combinations and ended up with ‘Predictry,’ which so far has resonated well with our audience.
SUB: What have the most significant challenges been so far to building the company?
Chua: Hiring good developers who are willing to come to Malaysia and to grow with the company.
SUB: How do you generate revenue or plan to generate revenue?
Chua: Paid by our clients. It can be monthly or cost-per-action, depending on agreement.
Currently it is free for at least the next couple of months, as we are challenging ourselves to process as much data [as possible]. So, any large global or regional ecommerce players are welcomed to be on our trial list.
SUB: What are your goals for Predictry over the next year or so?
Chua: Technical-wise, to continue working with our existing clients to maximize their ROI/business goals by improving our algorithms. We are looking to patent our algorithms within a year. As for business-wise, to obtain more clients, of course.