Big data once promised the world. But how much have things really moved on? Alex Aspinall investigates
It’s a few years now since big data revolutionised the way businesses marketed their services, developed their products and understood their place in the grand scheme of internet interactions. It’s hard to even remember what it was like back then, when a company’s data was unreliable, unusable and located in unhelpful silos. Imagine if you were still sending out mass communications, instead of treating every single prospect and customer as individual markets of one.
Of course, such a scenario reads like the stuff of fantasy for the vast majority of organisations (and that’s regardless of whether they are selling data servers to oil companies or biscuits to supermarket shoppers). Very simply, the average business is not yet able to take advantage of the mountains of data its prospects, customers and competitors are creating each day.
Big data, and the companies that had something to gain from making sure we all knew that it was the answer to everything, promised to change things for the better. But we are now left contemplating a scenario where its existence alone has changed nothing. Without the ability to interrogate and interpret the messages hidden in all this data, the fact that they exist means very little.
So, fully unburdened by rose tinted spectacles, we set out to understand exactly what ‘big data’ means for the B2B marketing industry right now.
Real life
Of course, the B2B marketing industry covers a broad range of companies and verticals, and for that reason it’s common to find quite a range of opinions knocking around. Experiences with big data are no exception: ranging from tales of successful experimentation, through to frustrations with inhouse abilities and outright scepticism regarding its usefulness and relevance.
Aaron Auld, CEO of data warehouse company EXASOL, neatly frames the discussion. Despite acknowledging there are examples of B2B brands moving ahead with big data solutions, he says: “Generally, big data is off to a bad start as far as mainstream B2B businesses go. The average B2B brand looks at this background with neither the resources to build such expensive IT strategies, nor the interest in getting down into their customer’s dirty laundry.”
Steve Grout, CEO of Tangent Snowball, sees a lack of understanding in B2B as being to blame for what he sees as low levels of adoption so far. He says: “In general, I don’t think B2B brands are embracing data enough; it’s traditionally been seen as more relevant to B2C brands. This is most likely because some B2B organisations still haven’t got their head around how to collect, manage and use data – and don’t have the expertise inhouse. Yet the brands embracing it are beginning to get a real advantage.
“I don’t think creating more detailed pictures of their targets is a pipedream, it’s just that too often organisations don’t really understand the difference it can make to the business, and therefore it’s seen as a nice to do rather than a must do.”
Others, however, explain the lack of impact big data has had in B2B circles as being more related to the fact that selling is more complicated in a B2B context. While a company selling consumer electronics will find it relatively easy to build a cross-section image of what I do and say online, the fact that decision making units in B2B are often much more complicated means the same company looking to sell to the business I work for will find the process significantly more challenging.
Anne Stagg, business development director at Communisis, is very much part of this camp, and adds: “Some B2B brands still face significant challenges in using big data to drive their marketing. These challenges are distinct from those in the B2C world, as the B2B sector has different complexities and demands. For example, clients can be based in multiple locations and offices, so it can be difficult to build a single clear view of that company.
“In addition, B2B brands generally have a small number of clients that are likely to be the source of much of their revenue. Traditional data analysis and segmentation tools developed for a B2C world, such as recency, frequency, monetary (RFM) analysis, are ineffective at identifying these high-value customers.”
Challenges and opportunities
A problem that regularly raises its head when new tech solutions come to the fore is that they rarely do so without implementation challenges that can seem to dwarf the supposed benefits. Once again, big data is no exception. While few would argue that being better informed about prospect and customer preferences, challenges and desires is an excellent position from which to address the market, the work that’s required to get there is significant. Indeed, it is beyond many time- and resource-stretched B2B brands.
Those companies that have started to take advantage of big data are generally larger in size, naturally technology-minded, willing to invest in new structures and reorganising how they operate.
Diane Blinkhorn, global marketing director at Bibby Financial Services, whose organisation is embracing mathematical modelling to address issues of profit and business decline, sees a willingness to develop new relationships and ways of working as being central to any organisation’s ability to adopt big data solutions.
She says: “It’s absolutely vital that you have a collaborative working relationship with the chief technical officer. Many marketers have the challenge of using data gathered from a range of internal systems that can’t speak to each other. So much time is wasted collating and shaping the data before any analysis and interpretation can be started.
“Marketers need to be sure the skills to analyse and interpret that data exist within the function. At Bibby Financial Services we invest in mathematics undergraduates who are looking to do a placement year by inviting them to work in our analytics team and then supporting them through their final year of university. We are then able to offer them a job once they graduate and therefore we are ensuring that we are building analytics skills in the business.”
Communisis’ Stagg also sees working with CRM and dedicated data professionals as being central to making big data work. But it’s not all about adopting how you work to suit big data. Big data should be able to make life easier for you too. Stagg highlights that adopting the intelligence hidden in big data can actually make justifying changes in working practices easier.
For example, she highlights big data as being a solution that should help businesses know how to work and what to focus on for maximum efficiency. She says: “The key to big data is using it to segment clients according to how valuable they are likely to be during their lifetime. This means identifying where there is headroom for further spending and allocating marketing resources as appropriate.”
Terms and conditions
Of course, we’re still talking in vague terms here. While some marketers may well be able to lean on their burgeoning relationship with the chief technical officer, others will feel isolated, as no such position will exist in their organisation. Similarly, the ability to refocus and dictate how their businesses conduct their operations might be limited or non-existent. We need to move forward to help shape a position from which all – or at least most – marketing departments can start to leverage the advantages we’ve all heard so much about. And current debate is failing to achieve this.
There are several shades of grey with big data. And in many ways that’s because the narrative around the subject, which can be seen as the collective outpourings of the media, frustrated marketers, those ploughing ahead with tales of interconnected success, and vendors with vested interests is rather confusing. It’s even questionable whether everyone is talking about the same thing when they type the words ‘big data’. This – clearly – is an issue that needs resolving.
Big data has become the default phrase for several things. Big data can refer to the phenomena brought about by digitisation, where we’re all leaving loads of information about ourselves all over the internet. But it can also refer to attempts to link up these individual pieces of information, as well as the platforms and businesses that have lined up to deal with it all. It’s something of a mess. And it’s very unhelpful.
John Bremer, group chief research and strategy officer at Toluna, points out: “The phrase ‘big data’ is used prolifically these days. What an individual means by the term can influence whether or not they think brands are making good use of it.
“For instance, if the term big data is used to describe the ability to process ever-increasing amounts of data in an effective manner, then for the most part companies are doing well. Further, if a company does not have the capabilities internally, it can easily buy it at ever-cheaper prices. However, if the term is intended to mean ‘the ability to connect diverse data streams together and analyse that information to gain greater insight that cannot be obtained via other means’ then brands are still struggling.”
This neat summary helps to put the problem into context. And it also highlights why when you start to speak to people about big data you can leave the conversation more confused than when you began. With everyone talking about slightly different things, we’re unlikely to move forward.
What might be helpful is a shift in focus. So far, all the emphasis has been on the data. We’ve all heard how it’s piling up: how we’re creating more of it every day now that we did in the first three thousand years of man’s existence, or whatever. And at first those images and stats were probably pretty helpful to shape an image of just how global and powerful all this is. But the mere existence of data, regardless of how much of it there is, isn’t really the thing we should be focusing on.
We need to move on from the obsession with big data, and start thinking about ‘big analytics’. It is important to understand the thing we’re all reacting to but if we concentrate the narrative around solutions and insights, the whole debate may suddenly become slightly more progressive.
Fergus Gloster, managing director for Europe at Marketo, shares this view of the importance of focusing on analytics, rather than just the data. He says: “The big data term is a broad umbrella term not restricted to marketing. Most modern digital platforms will result in the creation of large volumes of big data. How this data becomes useful is where analytics takes over. Marketers need the skills and the tools to provide in-depth analysis of all of the data being produced.”
The way forward
So, if we’re trying to trailblaze a path to a future where the benefits of the big data society are open to all, not just the privileged few, we need to establish exactly what can be done. And, despite the perennial problem being that there are very few one-size-fits-all solutions in B2B, it seems the most appropriate place to begin is – as ever – at the beginning, with an over view of your current situation.
Stagg says: “The challenge is to begin with the basics, putting data into a usable format so it allows B2B companies to identify where value sits within their customer base. This allows them to not only create tailored, proactive, outbound communications, but react effectively to inbound communications. The data analysis allows them to recognise what a ‘good’ client looks like and respond appropriately.”
This is sound advice; the focus on analytics and insight and the potential up-skilling and recruitment that may be required here only really comes in to its own once you can be confident you’re working with usable data sets: so, perhaps less ‘big data,’ more ‘your data’. In many ways, it’s encouraging to know that the best way to start making the most of the mountains of big data out there is to look internally.
A simplification of the big data narrative is definitely needed. And Marketo’s Gloster is willing to deliver here. He says there are only two things needed in order for marketers to go from a position of struggling to one where they are able to start utilising the piles of data available: they need the correct mindset and the correct platform. Once these are in place, the theory goes, that the future is there for the taking.
