Data-driven storytelling is a challenge and a goal for many a marketer, with the ability to customise recommendations to different audiences based on brand interaction near impossible, due to the blackbox nature of existing recommendation engines. ExchangeWire speak with Vijay Chittoor (pictured below), CEO, Blueshift, about how the launch of the 'Blueshift Personalization Studio' aims to change the landscape entirely and bring power not just to the marketers, but also to the data scientists.

ExchangeWire: The launch of the 'Blueshift Personalization Studio' allows marketers to roll their own recommendation engine – what does this mean and how is it of benefit to marketers?

Vijay Chittoor: Marketers have previously had to rely on IT teams to implement recommendation engines, and had no way to customise the recommendations to different audiences. Blueshift’s 'Personalization Studio' changes that, and puts the power of big data and machine learning into the hands of the marketer.

The platform is patent pending, so nothing like this exists elsewhere in the industry?

That's correct, no technology like this is currently on the market.

How will the 'Blueshift Personalization Studio' evolve how marketers are currently using recommendation engines?

Blueshift's 'Personalization Studio' evolves how marketers are using recommendations in a few different ways:

– Customise recommendations to the unique interactions that users have with your business: e.g. an online education company has very different user interactions on its website than an ecommerce company; in the past it has been easy for marketers to customise recommendations to match the nature of these interactions.

Vijay Chittoor, CEO, Blueshift

– Tailor your recommendations to the type of audience behaviour you are targeting: e.g. recommendations for an audience that abandoned a search yesterday can be based on what similar users did; whereas a different audience might be targeted based on their past category preferences instead. With 'Personalization Studio', marketers are in full control of tailoring recommendations to different audiences, instead of using generic widgets.

– Deliver your recommendations over multiple channels of marketing: in the past it has been hard for marketers to scale personalised recommendations to multi-channel marketing campaigns. Blueshift’s 'Personalization Studio' makes it easy to attach recommendations to marketing templates for various channels, and to execute personalised cross-channel campaigns.

With the platform being built on the Blueshift RSaaS (Rocket Science as a Service) platform, it is effectively eliminating the need for marketers to invest in data scientist resource. Are marketers attempting to develop recommendation engines in house? 

The goal is to empower both the marketer and the data scientist empowered to serve the customer:

– The marketer’s challenge is that they need to make their storytelling more data-driven. However, if recommendation engines are a blackbox, marketers are unable to understand them and unable to tell a story.

– The data scientist’s challenge is that it’s hard to keep their models updated with real-time data, and to seamlessly use data-science models in marketing campaigns.

Blueshift’s 'Personalization Studio' addresses both these challenges. Data scientists can access real-time data using Blueshift’s APIs and can submit their own recommendations into the system. Marketers can mix and match different types of recommendations to convey a personalised story to each and every customer.

In an effort to gain transparency and control, employing in-house data scientists is becoming a more common marketing phenomenon – how is RSaaS bucking this trend?

As mentioned above, Blueshift's solution empowers both marketers and data scientists to scale. It helps marketing organisations that don't have any data science resources, as well as organisations that leverage the combined power of marketing and data science teams.