Under the umbrella of “customer engagement” and “web engagement,” companies look to implement technology and processes to ride the next wave of the World Wide Web. After Web 2.0, companies now need to engage with their audiences.

And of course, it’s clear that visitors can no longer be treated as anonymous “guests” on a website, especially not when they have identified themselves by logging in. They should be recognized and serviced appropriately, with relevant information. And ideally, that experience should also be context sensitive, especially when someone uses a mobile device. But how do you make the content relevant?

When looking at B2B and B2C situations, it is important to consider the role of data analysis and recommendation engines, as well as asking the question of real-time versus batch, as the choice you’re making about that might have far-reaching consequences.

B2B vs. B2C: Aren’t We All Individuals?

There are many similarities between B2B and B2C engagement. In the past, B2C marketing was mainly about broadcasting, whereas B2B marketing used to be more direct and personal. But with the advent of the empowered and social consumer, consumers have become addressable as individuals as well. They are no longer anonymous faces in the crowd. Instead, they have a voice and a face. As a result, companies need to look for ways to adopt “mass personalization” in their marketing strategies. Apart from the fact that B2C personalization needs to deal with much larger volumes of people than B2B, there seemingly isn’t a huge difference between the two anymore. Or is there?

I Came to Your B2B Website, Now What?

In a B2B scenario, website visitors that identify themselves are likely to be stored in your CRM system or other back-end systems already, or they are candidates to end up there. Note that even though they are individuals, they are representatives of another company -- hence we call it B2B. Because the number of companies that a B2B business deals with is limited, we are talking relatively low numbers of visitors here.

In many cases we see B2B companies linking up their website with their back-end systems to achieve the following:

  • Capture leads (through web forms, for instance) and store them in a CRM system
  • Auto populate forms with data from the CRM or other systems
  • Update personal profile details (self service)
  • Check membership to grant or deny access to certain areas of the site
  • Personalize web content based on known information

The personalization of web content is often executed by establishing rules that are executed against someone’s profile and other known data, driving certain real-time decisions about which content to show on the site. Because the volumes are low and the amount of personalized content is usually limited, the desired real-time approach is usually very achievable.

What about B2C?

How different is this from a B2C scenario? Broadly speaking, the B2B approach still makes sense, but in addition we are talking many more potential website visitors. And companies will have a much bigger desire to promote specific products. A typical online retailer will have thousands of products, and in some way they will need to decide which products to showcase to the visitor during a visit in order to upsell or cross sell.

Of course, we can apply the visitor’s own explicit profile and historical behavioral data to decide which content to show, as well as implicitly generated data such as browsing behavior, time of day, the device used to connect to the site, place of origin and the local weather there, etc. But because we’re talking larger volumes of visitors, we suddenly have a unique opportunity to also apply learnings from how other consumers with a similar profile have behaved in the past.

Another Realm…

This suddenly takes us into a completely different realm of customer intelligence (CI) professionals. CI pros are more experienced than their interactive marketing counterparts in most data-related activities. They know how to use a number of customer data streams, such as transactional, customer feedback, insight from statistical models and offline segmentation data to support campaign planning. They excel at building customer and marketing databases for direct marketing use. This often involves activities such as data integrity, data hygiene, standardization and business rules design. And while interactive marketers typically rely on digital campaign performance analysis, CI professionals focus on broader marketing performance and customer growth initiatives.

They should now lend their services to apply the same techniques to mine for insights into data generated across all channels, including digital. This will allow interactive marketers to accurately apply richer data-driven insights in content optimization decisions and ultimately personalize web content.

Applying intelligent customer segmentation becomes an important step in this process. Given CI professionals’ experience with segmentation, they should reach out to their digital counterparts to collaborate on segmentation. During the data porting process, they can offer assistance matching between existing customer segments and online data. For example, matching in-store purchase behavior with online cookie data for retargeting is a valuable application of how marketers can improve targeting precision.

This, however, means huge amounts of existing customer data will need to be analyzed, and based on CI’s expertise, relevant profiles should be extracted and segments created. Such data analysis will typically lead to a batch-driven output process of all the relevant segments, which can then be fed into a website’s decision engine. Based on that, a visitor that matches Profile X can be served content that has proven to be relevant to people like him or her.

Current customer behavior on the website should ideally be fed back into this process in real-time, instantly affecting the website’s next action. The technology to do this completely and in real-time is still quite young though; the required infrastructure and processing power needed to do this should not be underestimated. A delay of a just few seconds is too long, so you will have to balance the need between this delay or a proper content recommendation based on the combined information gathered during a visitor’s last visit, with the intelligence gathered from analyzing the behavior of all other people with a similar profile.

A scenario of how this could work is depicted below:


In Conclusion

Marketers have long sought to deliver the right offer, at the right time, through the right marketing channel, to the right customer. The digitization of marketing starts to deliver on the promise to solve the relevance and personalization problem. However, marketers who throw technology at the real-time problem will still need to consider:

  • Many marketing processes remain in batch mode. Look carefully where you must be working in real-time, and where not;
  • Data analysts could suffer from analysis paralysis; with the onus on analysts to make sense of the data, which is now coming at them at a faster rate than ever before, you should focus first on getting the data where you need it in a way that you can digest it, analyze it and act on it, before trying to apply it;
  • Real-time enablement does not stop with a technology implementation. It has implications for the people using and accessing real-time tools, the processes in place to make real-time systems work, and the change management exercise required to shift from business as usual.

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