To avoid misleading customers, or simply through fear of legal backlash, advertising has evolved to hide the potential shortcomings of an offer in its disclaimer.
With all the hype that has accompanied the digitisation of consumer marketing, it is easy to forget that another important field of marketing has been going through its own digital revolution.
While it might not boast the same massive data sets as those wielded by consumer marketers, business-to-business marketing (B2B) has been making steady strides in improving how it captures and utilises data to fine-tune the sales process.
B2B marketing is benefiting from many of the same automation and analytic technologies and techniques being used in consumer marketing, but applied to a much smaller range of targets and in a one-to-one fashion that many consumer marketers might only dream of.
In B2B, what you do with your data is far more important than how much of it you have.
At the heart of the changes in B2B marketing is the fusion of big data analytics with marketing automation. These technologies are forging closer bonds between B2B marketing and sales teams, and at times blurring the lines between the two.
Big data database technology company, MapR, reports increased interest in its technology from B2B sales organisations. According to its US-based chief applications architect, Ted Dunning, this is especially true for companies with highly-complex product lines, where a single rep cannot possibly be aware of everything the company sells.
According to Dunning, this work represents an evolution of the original CRM systems that first appeared well over a decade ago, as big data technology brings predictive analytics to the rule-based responses of original CRM systems.
“Some of our customers have over a million products, and even to imagine that a person knows 1 per cent of the product line is absurd,” Dunning says. “But an automated system can keep track of all of the contacts between the company and all of the Web visits by the customer, and use that information to figure out when particular people are going to be receptive to making decisions about particular products.
“Predictive analytics can really help by saying ‘here are the five products that this person is likely to be interested in this quarter’. But none of this would have even been on the table if we didn’t already have CRM systems in place to capture this data.”
Greater interaction analysis
The key difference between old and new forms of CRM is the ability to analyse a far greater number of interactions between the buyer and supplier, across different channels and product lines. However, lengthy time lines and complexity of engagement (and particularly the potential for cross-contamination when conducting AB testing) makes determining overall success of these initiatives difficult, particularly if they are just measured in revenue terms.
Hence Dunning says it is important marketers find appropriate proxies to use as guides to long-term success.
“Revenues are so delayed as a measure here, you have to come up with surrogates in the modelling process that you can measure quickly and more accurately,” he explains. “You should be measuring all actions your customers take. The question then is which of those metrics actually represent surrogates.”
The most common surrogate is when and where a customer engages with the sales team or digital content. Dunning says this undisputable data can often be more reliable than the reports created by salespeople, who by nature are eternal optimists.
“As we get more and more data, we can look at it more and more seriously,” he says. “The triggers for sales are very complex and they are definitely multiple sources to the attribution and multiple indicators. And the differentiator between cause and effect is extraordinarily difficult in these settings. So this is the kind of problem where big data is very much required.”
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