Month: March 2014

The Heisenberg Uncertainty Principle of Social Media B2B Marketing

Reposted with minor modifications from the Selasdia Blog: http://www.selasdia.com/blog/?p=228

We have on numerous occasions come across B2B social media marketers pondering a seemingly inexplicable phenomenon.  Their social media leads just don’t convert!

Being a vendor of B2B marketing and sales tools we took a long deep think about it, and finally came up with a possible answer with a touch of quantum physics to it.

Here goes:

All our studies so far suggest that it’s possible to find leads, but that the leads will not convert into a sale immediately, that some time will elapse before a sale takes place.

That is in and of itself a very interesting phenomenon.  It appears that using social media, we can find the “who” or the “when”, but not the two together (at least not very often).

This observation reminded me of Heisenberg’s Uncertainty Principle in quantum physics. The principle is about a lower bound on the product of momentum and position of a particle.

Position (blue) and momentum (red) probability densities
Illustration of position (blue) and momentum (red) probability densities courtesy of Wikipedia

In other words, it is possible to tell where a particle is or how fast it is going as accurately as desired, but never both.

Our work on Selasdia for the past few years leads us to conjecture that there is something similar going on in the space of B2B lead generation using social media.

I don’t have an equation worked out that I can use to prove the conjecture.

But, I’ll describe two experiments that we ran and argue that what we observed in those experiments (and what others have observed in other sales conversion measurements) can be explained by the who/when uncertainty conjecture.

Let’s start with the experiments.

Experiment 1:  Nailing the When but not the Who

Selasdia is a robotic salesman that can build lists of potential buyers and support marketing and sales efforts with very focused nurturing.

In one of our experiments with Selasdia, we were able to use its intention analysis capabilities to spot prospects asking on social media for what one of our clients was selling.

[ For those who don’t know, intention analysis is a filtering capability centered around intentions, for example, an intention to purchase, inquire, complain, quit, etc.  Here’s ademo of intention analysis on our research lab’s web-page. ]

Well, it worked really well.  People were asking for that product quite frequently on social media and Selasdia was picking up as many as four requests each day.

4 leads a day sounds like bliss, right?  Well, wait till you hear what happened when the salespeople called up the leads.

When they called up the social media leads, they invariably found that the people who had asked for vendors on social media did not have a large enough budget (or were not willing to pay quite enough) for what they had asked for on social media.

So, here was a case of being able to tell “when” precisely someone experienced a pain or felt a need, but not being able to find such expressions of interest from the right people (the ones with the right budget).

Experiment 2:  Nailing the Who but not being able to tell When

In another engagement, a company that needed to sell a tool to online clothes retailers approached us.

Selasdia was able to automatically build a list of online clothes retailers and their CEOs. So, all the whos were known.

However, Selasdia’s intention analysis component was never able to catch any of the prospects expressing a need for the product on social media.

It was an example of a situation where we could tell “who” we needed to reach out to, but not “when” we needed to reach out to them.

Arguments in support of the uncertainty conjecture

In a previous article titled “Learning from our failures – two reasons why social media sales conversion rates could be low” we proposed an explanation for the outcome of Experiment 1.

We proposed that people who know the value of what they want would not use a process where a vendor would have to find them and would instead actively look for a vendor.

So, people posting their needs on social media probably don’t take it very seriously.  That would explain the low conversion rates.

[Note that the poor conversion rate of social media marketing was noted in an article by an agency called Inside Sales a while ago (http://www.insidesales.com/insider/lead-generation/why-social-media-is-overrated-for-lead-generation).]

We’ve tried to establish through our reasoning so far that having accurate knowledge of the when (knowledge of an explicitly expressed need) might be only half the battle.

That is because the person who expressed the need might not be willing to spend a lot of money on it.

Now regarding Experiment 2, we noticed that as the ticket value of the item being sold goes up, the probability of seeing someone asking for it on social media seems to go down.

So, the cost of the item being sold seems to be inversely related to the number of buyers asking publicly for it.

For very expensive offerings (typical of B2B software sales) there is a very small set of buyers who need it and who can take a decision on buying it.

So, it is easy to make a list of them (as shown in Experiment 2), to prioritize them and to reach out to them.

So we can build comprehensive lists of the whos to sell to.

But the whens become difficult to determine (because high-priced purchases don’t get discussed openly on social media).

Explanation in terms of BANT

A popular set of criteria used for B2B lead qualification is the BANT criteria.

BANT stands for Budget, Authority, Need and Timing.

The Budget and Authority concepts attach to the person / the who.  Does the person who might make the purchase have the budget and the authority?

The Need and Timing concepts attach to the time / the when.  Does the person have a need at this time?  If not, when will the person have the need?

So, we can break down the BANT criteria into BA and NT as follows:

BA = who

NT = when

What we have argued above is that through social media marketing alone, it might be difficult to find both the BA (the who) and NT (the when) at the same time.

So what can a B2B marketer do in the absence of one half of the BANT qualifying criteria for lead generation?

Strategies for Compensation

Strategy 1:  One strategy for compensation is nurturing.

When the BA is known but the NT is not, it makes sense to nurture the prospect for a suitable period of time.

The idea is that by engaging prospects who fulfill the BA requirements of the BANT qualification criteria, it is possible to make them aware of their need and that they might pick the brand that they came most in contact with, when they eventually realize that they have a need (the NT part of the BANT criteria).

This is typically a strategy that the marketing team would execute.  Any leads resulting from the strategy would come in through the inbound marketing channels that have been put in place.

Strategy 2:  Another strategy for compensation is switching channels

Since there is evidence that social media channels result in low conversion, the strategy is you to switch from social media to email channels for pursuing a lead where the need and the timing are known.

Since it is not known who the right decision maker is, it makes sense to analyse the decision makers in the organization and then to approach the most suitable ones through email.

This is typically a strategy that the marketing team would execute with the help of the inside sales team of an organization.

Selasdia’s Support for Compensation Strategies

In support of Strategy 1, Selasdia now not only helps B2B marketing/sales teams build lists of customers, but also supports nurturing strategies that are very tightly integrated with content publishing and social listening channels.

In support of Strategy 2, Selasdia now has information gathering processes that can locate decision makers who are not on social media and can help engage them using email in addition to social media.