5 Big Changes in B2B Buying Behavior

If you’re a B2B marketer — especially a services provider — your environment is about to be upended. Customers are changing, and so are the ways they buy. I’ve been struck recently by five glaring developments in business buying behavior that you need to know about.

If you’re a B2B marketer — especially a services provider — your environment is about to be upended. Customers are changing, and so are the ways they buy. I’ve been struck recently by five glaring developments in business buying behavior that you need to know about.

And once you know, you must consider how to adapt and, better yet, turn the changes to your advantage. Consider these.

The Arrival of Millennials in Business Buying Positions

These 30-somethings are rapidly migrating from researcher and specifier into decision-making roles. I’ve written about this before, offering ideas for how marketers can cope. But I also see this development as part of a larger trend that has deep implications for how we need to be selling and marketing today.

Use of Ratings and Reviews Sites in B2B

Comparison sites in the mold of TripAdvisor and Yelp have entered the B2B buying process; especially in crowded categories, like software and services. You’ll find ratings sites like TrustRadius, Capterra (now owned by Gartner), Clutch.co and G2Crowd, where users leave product reviews — and sellers quake in their boots. Here are some tips for how marketers can take advantage of this new channel.

Expanded Customer Requirements for Compliance

Long prevalent in government buying, companies of all sizes are increasing their requirements of vendors in areas such as sustainability, diversity, and — for manufacturers in such categories as apparel — wages, working conditions, and safety. Christine Crandell brought this to my attention recently, with examples like Marriott embracing the UN 17 Sustainable Development Goals 2030 as a source of competitive differentiation, and how event planners are routinely making venue carbon footprints and greenhouse gas emissions an evaluating criterion in property selection.

Buyers Are Bringing Their Consumer Expectations With Them to Work

They want fast, personalized service, pricing transparency, ease of use, a human face, seamless integration across contact channels, and mobile access. We know this, but are we stepping up?

Enterprise Buying Platforms Mature

B2B buying has long been enabled by EDI, supplier exchanges, and e-procurement. But the pace is accelerating — fast. A new entrant is Globality’s platform that helps large enterprises buy services. According to Kathy Chin Makranyi, head of corporate marketing, Globality’s founders recognized that services procurement is inefficient, and ripe for change. So, they set up an AI-enabled platform that manages the entire buying process, enabling buyers to write the RFP, identify a short list of candidates — even inviting incumbents to participate, conduct the bidding process, hire for and manage the project, and handle the billing. Globality has vetted and recruited over 17,000 services providers to the platform, giving enterprises access to entirely new potential vendors. And the platform saves both time and money in managing the competitive bidding process.

“It’s a marketplace between the global 500 and a network of worldwide providers. The big services firms, the McKinseys, KPMGs, and Accentures will play, too, because it makes their sales cycles faster and easier. If you go with the incumbent, you’ve confirmed they are the best choice. Sourcing team[s] can learn and validate their work. And a provider who lost can find out ways to improve next time,” explains Makranyi.

Calling all consultants, accountants, lawyers, agencies — here’s your chance to compete on a level playing field for enterprise accounts.

 

A version of this article appeared in Biznology, the digital marketing blog.

Marketing Success Sans ‘Every Breath They Take, Every Move They Make’

Last month, I talked about how to measure success when there are many conflicting goals and available metrics flying around (refer to “Marketing Success Metrics: Response or Dollars?”). This time, let’s start thinking about how to act on data and intelligence that we’ve gathered. And that means we get to touch different kinds of advanced analytics.

Last month, I talked about how to measure success when there are many conflicting goals and available metrics flying around (refer to “Marketing Success Metrics: Response or Dollars?”). This time, let’s start thinking about how to act on data and intelligence that we’ve gathered. And that means we get to touch different kinds of advanced analytics.

But before we get into boring analytics talk, citing words like “predictive analytics” and “segmentation,” let’s talk about what kind of data are required to make predictions better and more accurate. After all, no data, no analytics.

I often get questions like what the “best” kind of data are. And my answer is, to the inquirer’s disappointment, “it depends.” It really depends on what you are trying to predict, or ultimately, do. If you would like to have an accurate forecast of futures sales, such an effort calls for a past sales history (but not necessarily on an individual or transactional level); past and current marcom spending by channel; web and other channel traffic data; and environmental data, such as economic indicators, just to start off.

Conversely, if you’d like to predict an individual’s product affinity, preferred offer types or likelihood to respond to certain promotion types, such predictive modeling requires data about the past behavior of the target. And that word “behavior” may evoke different responses, even among seasoned marketers. Yes, we are all reflections of our past behavior, but what does that mean? Every breath you take, every move you make?

Thanks to the Big Data hype a few years back, many now believe that we should just collect anything and everything about everybody. Surely, cost for data collection, storage and maintenance has decreased quite a bit over the years, but that doesn’t mean that we should just hoard data mindlessly. Because you may be deferring inevitable data hygiene, standardization, categorization and consolidation to future users — or machines — who must sort out unorganized and unrefined data and provide applicable insights.

So, going back to that question of what makes up data about human behavior, let’s define what that means in a categorical fashion. With proliferation of digital data collection and analytics, the definition of behavioral data has expanded considerably.

In short, what people casually refer to as “behavioral data” may include this to measure success:

  • Online Behavior: Web data regarding click, view and other shopping behavior.
  • Purchase: Transactional data, made of who, what, when, how much and through what channel.
  • Response: Response history, in relation to specific promotions, covering open, click-through, opt-out, view, shopping basket, conversion/transaction. Offline response may be as simple as product purchase.
  • Channel: Channel usage data, not necessarily limited to shopping behavior.
  • Payment: Payment and related delinquent history — essential for credit purchases and continuity and subscription businesses.
  • Communication: Call, chat or other communication log data, positive or negative in nature.
  • Movement: Physical proximity or movement data, in store or store area, for example.
  • Survey: Responses to various surveys.
  • Opt-in/Opt-out: Sign-up specific 2-way communications and channel specific opt-out requests.
  • Social Media: Product review, social media posting and product/service-related sentiment data.

I am sure some will think of more categories. But before we create an exhaustive list of data types, let’s pause and think about what we are trying to do here.

First off, all of these data traceable to a person are being collected for one major reason (at least for marketers): To sell more things to them. If the goal is to predict the who, what, when and why of buying behavior, do we really need all of this?

The ‘Who’ of Buying Behavior

In the prediction business, predicting “who” (as in “who will buy this product?”) is the simplest kind of action. We’d need some PII (personally identifiable information) that can link to buying behaviors of the target. After all, the whole modeling technique was invented to rank target individuals and set up contact priority — in that order. Like sending expensive catalogs only to high-score individuals, in terms of “likely to respond,” or sales teams contacting high “likely to convert” targets as priorities in B2B businesses.

The ‘What’ of Buying Behavior

The next difficulty level lies with the prediction of “what” (as in “what is that target individual going to buy next?”). This type of prediction is generally a hit-or-miss, so even mighty Amazon displays multiple product offers at the end of a successful transaction, by saying “Customers who purchased this item are also interested in these products.” Such a gentle push, based on collaborative filtering, requires massive purchase history by many buyers to be effective. But, provided with ample amounts of data, it is not terribly difficult, and the risk of being wrong is relatively low. Pinpointing the very next product for 1:1 messaging can be challenging, but product basket analysis can easily lead to popular combinations of products, at the minimum.