TargetingMantra launches a new product to make marketing emails less spammy and more effective

I usually ignore the promotional junk mail about sales and deals I get in my email each morning. But my friend, a big shopaholic, never misses out on them. And when she starts a purchase but then abandons it, reminder emails sometimes get her to go back and complete the sale. So, wouldn’t it be a good idea for estores to target shoppers like her, rather than wasting their resources and emails on me? That’s the concept behind TargetingMantra, an Indian startup that wants to make email marketing more precise.

After helping optimize Amazon’s recommendations with machine learning, then setting out to apply that same tech on a broader scale, TargetingMantra’s founders raised US$1.1 million in seed funding and are now ready to launch their first product. Snowflake Email Marketing Solution goes live today.

Snowflakes are beautiful, pure, and white, and no two are alike

Snowflake is a solution for ecommerce and online businesses that helps them reach out to consumers at the right time with the right product. “Just like each snowflake is unique, no two users are the same in terms of their interests and requirements. So, instead of bombarding the same email to every user or even a segment of users, our platform ensures that each user is treated uniquely and gets customized mails based on his/her interest and at the most relevant time,” TargetingMantra founder and CEO Saurabh Nangia told Tech in Asia.

For example; let’s assume a particular user checks her email around 8:00 am, shops during the first week of every month, and was last browsing through a scarf collection in an estore. If that particular estore sends this user an email with various scarf recommendations during the first week of the month at around 8:00 in the morning, it most certainly increases the chances of that user opening the email, liking the recommendations, and ultimately buying one or more scarves.

So instead of spamming all users everyday with generic campaigns, consumers get relevant emails only when they are likely to open them. “Sending emails to only relevant users can help companies save money and avoid the reputation of being spammers. Companies can even lose their existing customer-base by bombarding them with emails. Therefore, we choose the users who are most likely to open an email at a particular time and go ahead and shop for something. Targeting these users at a relevant time can be productive for an ecommerce company,” says Nangia.

The choice algorithm

So, how does TargetingMantra gather all the intel needed for precise user targeting? It uses “omni-channel personalization suite” (an ensemble of multiple machine learning algorithms) to track online buying behavior and mine insights. With this user-behavior data, it offers recommendations to different consumers, customized on the basis of their interests and requirements. It uses the same data for Snowflake Email Solution; the insights are given to the marketing managers, so that they can choose which marketing campaigns to create, which users to target, and what content to be sent.

Talking about the ease of using this platform, Nangia states that companies are generally worried about integrating a new platform into their system because of the time involved. However, in this case, companies can leverage the utility of this platform in a single day. All it takes is a single engineer and this platform can be integrated into the company’s system within 24 hours.

Last year, TargetingMantra raised US$1.1 million in seed funding from 500 Startups, Nexus Venture Partners, and One97 Mobility Fund. A major chunk of the funding has been used to grow the team. It has also helped the startup to expand into different regions. “Today, we have clients in the US, India, Brazil, Singapore, Japan, and China. [The money] has also allowed us to create this new marketing automation platform, where email is our first target medium. We will be targeting more channels for ecommerce companies to reach out to their end users in the coming days,” says Nangia.

TargetingMantra does face competition from a number of other Southeast Asian startups in this space. Singapore-based Linkcious, founded by serial entrepreneur Weichang Lee (disclosure: Lai owns a tiny amount of equity in Tech in Asia) and computer engineer Jason Tan, is a product recommendation engine for estores that uses artificial intelligence for automated related recommendations; much similar to what Amazon does.

Israel-based Dynamic Yield is another major player in this niche marketing space. It provides website personalization and automated conversion optimization tools for marketers, retailers and publishers. It allows every website visitor to automatically see the right content, marketing campaigns, and layout, customized for him/her. Backed by Bessemer Venture Partners, it raised US$12 million from Marker LLC last year.