bang for your buck
April 22, 2016

When you can get most ‘bang for your buck’ out of analytics

By Paul Laughlin

Over many years creating, leading or advising insight teams, I’m often surprised which analytics has the most impact. Often it’s not the most technically complex, nor the one identifying the largest potential profit opportunity, that results in greatest engagement & action. Most ‘bang for your buck‘, more often lies in the ‘right timing’.

What I mean, by that phrase, is analytics that was shared at the right time (when that challenge or opportunity is ‘front of mind’ for senior leaders).

I’ve made similar comments in the past. So, as part of our April theme of analytics, I thought it might help to share some specific examples to bring this to life.

The following examples have been selected at random from those I remember. But, hopefully sharing them will spark some ideas or at least provide some encouragement; to other insight leaders planning how to best launch new analysis with senior committees.

Bang for buck 1: Segmentation

The first example that springs to mind, is developing an attitudinal segmentation for a general insurance business.

What set this apart, from so many past unused attitudinal segmentations, was not the richness of segment/persona insights (although this based on large quant exercise, factor analysis & clustering) – it was the overlay. Blessed with a richness of transactional & demographic data, the customer data view we pulled together made it possible to reasonably accurately predict attitudinal segment allocation for the whole customer base. One tip when doing this. The statistics on fit will never look great, but be just as concerned with the distinct profiles of the segments allocated. If their behaviour & attitudes once re-surveyed do align with those expected from that attitudinal segment, then you have a useful proxy.

Despite segmentations often being one of the pieces of analysis of more interest to senior leaders, it was two other aspects of this work which sealed their engagement. The first was visual. Bothering to do the work to create engaging visualisations, personas, videos with actors & even life-sized cut-outs – all this work paid dividends in engaging the board during immersion sessions.

But it was one simple graphic that captured the CEO imagination & helped shape business strategy for years to come. Learning from the old Boston Consulting Group passion for 2×2 matrices, positioning 8 attitudinal segments on a bubble chart of ‘self-directedness‘ against ‘ability to retain‘ was a winner. It came about from 1-2-1 chats with the CEO about his thinking, plans & questions for the segments. Well worth knowing your audience & producing one graphic that ‘speaks their language’.

Bang for buck 2: ‘Income at risk’¬†Analysis

A second example, of getting more bang for your buck, was less complex analysis. This time, the foundation was retention analysis of one product line for a leading bank. That simple analysis, of competitor to whom those cancelling were going, identified a need to engage colleagues across the bank with the importance of not losing these customers. However, this was not a high priority product for the bank at the time.

As with the previous example, it was the combination of analysis from multiple sources that led to the ‘eureka‘ moment. This time the other source was competitor analysis. Benefiting from having market & competitor analysts in the same team, helped identify that this competitor was rapidly growing their market share of another line (that was a priority to both organisations). With that idea in our heads, further transactional analysis showed that those customers lost to the former product did then show a tendency to later cancel this higher priority product (moving that as well).

Summarising this, in a way that engaged & motivated action, was just a matter of calculating the income at risk if the bank continued to lose customers to this competitor. Adding up the income at risk for both products (appropriately weighted), made for a much scarier & thus more engaging number. So, a one page summary could convey this ‘burning platform‘.

Bang for buck 3: Media interdependencies

My last example, for now, is from the field of marketing effectiveness measurement. Work with the ‘direct to consumer’ wing of a large UK insurer, had generated a useful suite of marketing effectiveness reporting (expanded to include ATL with econometrics, as well as use of control groups for BTL ‘uplift’ measurement). That yielded a number of benefits and enabled marketing spend to be redirected and in some cases cut to deliver cost saving targets.

However, there were still some media (like the infamous ‘door drops’) which research indicated were irritating customers, but persisted. They survived, basically, by being cheap. Another concern was a very channel silo’d mentality across this organisation (with some channels secretly viewing those other channels, of same brand, as their primary competitor). Something needed to be done to help break down this fiefdom thinking.

What suddenly struck us was the lack of any marketing performance reporting that considered the reality that customers had been exposed to more than one media. In these days of more sophisticated ‘marketing attribution’ that may sound primitive, but plenty of organisations are still at this stage.

The breakthrough analytics in this case was termed ‘media interdependencies’. Based on household match-back (rather than relying on tagging), it analysed the media effectiveness of combinations of media, not just solus reporting. Such a simple principle yielded some breakthrough findings. We were able to show that ‘door-drops’ were suppressing natural level of response in other channels (due to annoying existing customers that much) & that some of the feared digital channels were uplifting response in face-to-face meetings.

What lessons have we learnt?

I hope a few common themes come out of those three recollections for you. Three strike me:

  1. It’s normally by bringing together evidence from other sources (analysis, research, market intel, other products, other channels) that insights are discovered;
  2. Knowing what matters to the organisation & leaders at the time helps you focus on what insights to share & when;
  3. Taking time out to step-back & think about the real-world experience of customers & colleagues often helps you see a new way to approach the problem.

What about you? Where did you get most bang for your buck? Which pieces of analytics from your teams have taken-off disproportionately?

Have a good week & keep pushing for impact from your insights.