Every now and again a startup comes along that is doing something really great — a fledgling business both exciting and promising, and yet, it seems to be having a really hard time at making people understand the value of its product.
No ad to show here.
In TargetingMantra‘s case, it might have something to do with its elevator pitch. The startup from Gurgaon, India, bills itself as a “one-stop solution for personalisation, targeting, and big data analytics needs of online businesses.”
So what the company is actually trying to say, is that it runs a bunch of services that can make better product recommendations to your ecommerce customers.
Amazon.com probably has the king of recommendations systems. Once you buy something, the site’s eerily intelligent “related items” algorithm will lure you back with purchase-relevant products that you will find exceedingly hard to resist.
If only ecommerce businesses could all have Amazon-level recommendation engines. Well, it just so happens that TargetingMantra co-founder, Saurabh Nangia, is an ex-Amazonian. At Amazon, Nangia designed and implemented a distributed similarity engine framework for Amazon.com subsidiaries — IMDb, Lovefilm, Audible, Shopbop and Zappos. The result was a significant increase in ratings and purchases.
Nangia has five patents pending in the recommendations domain and holds a Masters in computer science, specialising in machine learning from the University of Illinois Urbana-Champaign (USA) and a BTech in the same programme from IIT Guwahati (India).
TargetingMantra, currently in private beta since March 2013, will launch with over 15 solutions which include widgets for “Similar items”, “People who bought also bought”, “Frequently bought together”, “Recommendation Emails” and “Banner Ads”.
The service works through a one time integration effort that Nangia promises will take around one to two days of work on the clients’ end. He says that clients can expect to recoup at least 10% of annual revenue which would have otherwise been lost. Nangia reckons it would take a company close to two years to develop an in-house system as powerful as TargetingMantra’s.
Companies can also track their performance and make strategic decisions through TargetingMantra’s analytics dashboard.
TargetingMantra’s revenue model includes pricing plans depending on feature requirements, the amount of traffic to a client’s website, catalogue size and revenue generated through TargetingMantra.
Based on the startup’s own estimates, its average revenue from smaller firms will be around US$2000 per month, US$5000 per month for a medium-sized firm and about US$20 000 per month for large clients in the Asian and American markets.
The idea for TargetingMantra came about while Nangia was working at Amazon in Seattle. Nangia found that personalisation and targeting in most of the SMEs — particularly with ecommerce and media flavours — in the emerging markets wasn’t up to scratch. He moved back to India and bootstrapped the company with the help of co-founder Rahul Singh, who heads up a string of aspects including marketing, market entry and growth strategy, internal corporate strategy, market assessment, techno-economic feasibility, due diligence and business process innovation.
Singh worked as a senior consultant with Cedar Management Consulting, and as a technical specialist at Ericsson in Sweden. He has a B.E. in electrical and electronics, an MSc in Physics from BITS, Pilani and an MBA with specialisation in marketing from SP Jain (Dubai & Singapore).
TargetingMantra doesn’t appear to have serious, direct competition in India, but could face off internationally with RichRelevance, Strands Recommender and Barilliance.
Nangia and Singh believe that their one-stop approach — personalisation, big data analytics and targeting solutions — as well as “best-in-class algorithm” and analytics proposition will make TargetingMantra stand out.
To start, the duo is targeting only ecommerce companies in India. Starting next year, they want to focus on the hundreds of ecommerce companies in South Asia (not including China).
“The market for our services in Asia is expected to grow tenfold in the next two to three years. After this, there is also the African, European and American markets to tap into,” says Nangia.
Nangia and Singh admit that the company faces challenges — TargetingMantra’s calculated rollout means that it’s slower to jump onto Asian markets.
“The delay in getting our first few clients in the other markets of Asia before we go for full-scale marketing may give the edge to our competitors,” says Singh.
Nangia adds that good help is hard to find, “good engineers who understand machine learning and big data analytics is both difficult and expensive.”
Finally, the duo believes that customers in the region are not mature enough in their life cycles to appreciate the importance of TargetingMantra’s services.
Despite the challenges, TargetingMantra is shooting for the stars. On the way to its target of 300 clients and revenue of US$200-million in five years, TargetingMantra’s strategy will include targeting companies whose investors have invested in multi-national ecommerce and media companies, making cross-border entry easier.
