Bridging the Digital Gap between B2B and B2C Marketing with Big Data

With the blooming of Big Data in recent years, there are a number of success stories in business, particular in B2C companies who engage with consumers directly in their everyday lives. One of the most well known examples is the way Netflix uses big data analytics to create House of Cards.

Through the rich amount of user data stored and analyzed by the company, Netflix found that movies from David Fincher, the director of The Social Network, are not only popular but also constantly being watched from the beginning to the end. Undoubtedly this is a good indication of how well the content hooks the users. In addition, it is also observed that films featuring Kevin Spacey perform well on Netflix. With that in mind, Netflix looked into Kevin Spacey’s fans and found that the British version of House of Cards is popular among the fans as well. Combining these three insights found with big data — David Fincher, Kevin Spacey, and the British version of House of Cards, Netflix created the House of Cards show and recommended it to relavent target audience, resulting in a 9/10 rating on IMDb for the show.

More importantly, Netflix forms a stronger bond with its users, through tailored contents supported by big data analytics. Could B2B players utilize a similar approach to deliver personalized marketing tactics, case studies, and product demos, in hopes to make brands matter more in the daily lives of customers?

The short answer is Yes.

The longer answer requires us to first examine the digital gap between B2B and B2C companies. In 2016, McKinsey surveyed 47 B2B and 128 B2C companies, decomposing the gap into strategy, culture, and capability.

Strategy — B2B are behind in using digital tools and data to set strategy

Only 10% of the B2B companies see digitalization along with digital strategy as one of their top three investment priorities, compared with 20% of the B2C companies. In critical customer-facing areas such as mobile, only 6% of the B2B companies report to have a mobile strategy, compared with 30% of the B2C companies.

Culture — B2B are behind in taking concrete steps towards digitalization

Only 25% B2B companies said their leadership communicates digital strategy clearly, along with confusion about digital roles and lack of ownership on digital initiatives. In addition, fewer than 15% of B2B companies adopted test-and-learn approaches to new digital initiatives and it often takes longer than a year to implement such approaches.

Capability — B2B are behind in offering cross-channel experiences

B2B companies are not taking full advantages of social media and content creation. Unlike their B2C counterparts, they also lack of advanced analytics capabilities to automate decisions towards customer-facing situations.

Now, from a marketing point of view, how can the gap be bridged?

The key lies in thinking differently about B2B digital marketing campaigns. As opposed to treating it as a descriptive analytics problem, we should solve it as a user centric optimization problem.

Traditional Approach — Descriptive Analytics

In traditional B2B digital campaigns, we first identify our target audience, set communication and platform strategies, and create a set of tactics such as eDMs, display ads, SEM keywords, Facebook posts, and landing pages for each campaign. These creative tactics are then delivered to the audience through chosen media channels, so audience can visit the landing page to further engage the brand with pre-selected offers such as case studies, white paper pdfs, and product demos. After a couple of days, we will evaluate media channel and landing page performance with key metrics including impressions, clicks, visits, bounce rate, and registrations. This is also known as the digital funnel, as we can benchmark all the metrics based on previous campaigns and identify the underperforming metrics. We can then adjust current campaigns or apply learnings to future campaigns.

Optimization Approach — Predictive Analytics

With a user centric optimization approach, instead of designing and executing a series of campaigns for individual products, we should first create libraries to store creative tactics and offers in as many formats as possible, and deliver them to users through an optimization engine. The optimization engine uses big data to constantly run small scale tests and continuously predict different combinations of creative tactics, media channels, and offers that will yield the best results, in terms of visits, conversions, and revenue, for individual users. The big data here for B2B companies could be broken down into three categories of attributes.

Tactic
These are the information about the tactics used in the campaigns, such as media channel performance data, creative size and format, promotion timing and seasonality data.

Offer
These are the information on offers, including their formats, intended customer journey stages, product line, and product price range.

Audience
These are the information that we have on individual user, including demographic data, transactional data, web behavior data, and social behavior data.

The takeaway here is being user centric. Because of the advancements in big data technologies, companies now have access to individual user data in different formats and from multiple data sources. It is evident that B2C companies are ahead in the game, but as B2B players begin to invest more resources and adjust their priorities, the line between B2B and B2c will blur, forming a new generation of Business-to-Human (B2H) enterprises.