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B2B vs B2C marketing analytics – the same, but different?

analytics illustrationI’ve spent much of my career working in data-driven marketing roles and delivering insights for B2C brands, but over the last decade the balance has shifted and I now work almost exclusively with B2B businesses. While some of the differences between the two worlds are to be expected, such as the availability of different data types and the more complex buying cycle in B2B, in fact there are a great deal of similarities in the techniques and types of analysis that can be carried out for B2C and B2B. So why aren’t B2B brands making as much use of analytics as their B2C counterparts?

At first I wondered if this was just my isolated view of the world, but a recent study* by B2B Marketing in association with Circleresearch seems to support this. It reveals that 73% of B2B marketers don’t feel their companies make the most of data, with the weakest skills being in the area of Predictive Analytics.

Not a day goes by where we don’t carry out one or more of the following analyses for the B2C brands we work with:

  • Upsell propensity modelling
  • Path to high value analysis
  • Segmentation
  • Share of wallet analysis
  • Cross-sell propensity modelling
  • Churn prediction

And yet, I still don’t see widespread adoption within B2B organisations. Of course there are exceptions, and some readers will be able to recount many examples of insight-driven B2B sales and marketing activities they’ve been part of. But it’s not commonplace.

In simple terms, the marketing objectives facing B2B and B2C businesses are the same. The difference however, is that B2B businesses tend to focus their efforts on acquisition activity, with much less attention given to “in-life” marketing. B2B buyer journeys are much more complex, lead times are longer, and involve multiple decision makers and influencers (Prospect Modelling and Lead Scoring are great examples of analytics used here, particularly with the tools that have been developed in the last 10 years).

While it used to be true that the low volume and variety of data was a limiting factor in B2B, this is no longer true. Data collected through inbound marketing activity and social channels is a rich and current asset, and the tools and platforms available now mean we can quickly convert this into insight.

Here are just a few ways that analyses most often used for B2C can inform marketing programs for B2B organizations:

  • Churn Prediction: Develop a model that predicts which customers are more likely to churn at the end of their contract. For B2B knowing who to contact, and with what message, has historically been tricky. However, analysis of previous contact behaviour and campaign interactions can help optimise future activity and identify the key decision makers.
  • Share of Wallet: Share of Wallet analytics is an area that has great potential in B2B. Most B2B organisations have an account management structure to maintain the relationship with their customers, and this typically means that high value accounts get 1:1 attention while low value accounts just become one of many for a beleaguered account manager. A Share of Wallet analysis can identify those accounts that still have room for growth, and will typically unearth some sleeping giants!
  • Path to High Value: We use this a lot with our B2C clients! Look at today’s ‘best’ customers and identify what was the sequence of events that got them there. Is their first purchase significant, or is it the acquisition channel, or just their firmographics? By recognising tomorrow’s best customers at an early stage, you can implement the right programs that will help nurture that growth

To put all of this into perspective, research by eConsultancy in association with Adobe** shows that only 26% of responding (B2C and B2B) organisations have a solid data-driven marketing strategy in place, so perhaps it’s no surprise that analytics isn’t as widespread in B2B as I’d hope to see. There’s definitely still room for improvement on both sides, so maybe the similarities are greater than I thought!

Harte Hanks has a team of Analysts, Data Scientists and Strategists to help you integrate analytics into your B2B sales and marketing plans. Is your company fully utilizing Analytic driven insights to better inform acquisition, growth and retention activities? Tweet us at @HarteHanks and share your experience.

* “Data Skills Benchmarking Report 2016-17”, April 2016
** “Quarterly Digital Intelligence Briefing: The Pursuit of Data-Driven Maturity”, April 2016

Back to the Future: Predictive Analytics

Predictive-Analytics_HarteHanks

What if you knew what your customers wanted, when they wanted it? With predictive marketing analytics, gazing into the future is entirely possible. While predictive analytics is not a new concept – marketers have often tried to use past performance to predict future behavior – the dawn of the information age has amplified its effectiveness and usability. Predictive analytics allow marketers to focus efforts and maximize their budgets by identifying targets who are ready to buy and by eliminating those who aren’t.

Big Data

 To accurately predict consumer behavior, you need more than focus groups and surveys. The era of Big Data has armed marketers with a deluge of information on consumers – including engagement with marketing automation platforms and “intent” data from across the web. The technology to crunch this data and make sense of it is rapidly evolving, providing marketers with a roadmap to reach the right audience at the right time.

Data in Action

The Big Data era has produced an incredible amount of information about habits, desires and tendencies of consumers. Marketers who follow these digital footprints can optimize their marketing efforts to target individual audience segments and personalize messages to speak directly to potential customers. Predictive analytics can help create incredibly specific buyer personas – marketers no longer need to rely on broad demographic data and guestimates of what a particular buyer prefers. Enhanced buyer personas lay the groundwork for highly personalized messaging for nurture campaigns, which multiple studies show leads to significant increases in conversion and revenue. Predictive analytics also provide the benefit of targeted spending. Knowing what audiences to target and which platforms to target them through significantly increases the impact of marketing budgets.

B2B Adoption

B2B marketers have lagged behind their B2C counterparts in the adoption of marketing technology ­­– predictive analytics included. And while it’s true that personalized data from individual consumers offer a more clear view into purchasing habits and tendencies, plenty of data exists for B2B customers that can be utilized to implement more intelligent marketing tactics. Purchase history, for instance, is a great predictor of current and future behavior. If a customer has recently purchased a software system that won’t need an upgrade for three years, targeting that customer with marketing messages is not only inefficient, but could negatively affect that customers’ perception of your brand. Existing software licenses, log-in frequency, help desk calls and firmographics can also help B2B companies predict the need and desire for their products. Normally this kind of data will predict the type of customers that buy your products. Add social data sources to the mix, and you can predict customers that are ready to buy.

Implementation

Depending on the level of sophistication and budget resources, B2B marketers can deploy analyst-led solutions or automated “black box” solutions to perform predictive analytics. For larger, more comprehensive data operations, an analyst-led approach is preferred. Computers are wonderful, but a human touch – specifically when there are oddities in the data – can more accurately utilize the information output to design programs and messaging that take into account both the customer and the nuances of the company. However, there are various automated solutions that are more than sufficient for less sophisticated marketing automation programs. Both approaches have their own merit, but one thing is clear: predictive analytics allow businesses to focus on what’s important and discard what’s not, leading to amplified revenue growth – and happy customers.

 

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