Your Data Strategy Needs to be Scrapped:

Here’s How to Fix It

June 11, 2020

By Jonny Bentwood, Head of Data and Analytics

Every company that has built a solid data modeling strategy that uses the finest algorithms to predict sales needs to stop. Every firm that has invested years of time into refining their business intelligence engine that uses using historical data to inform them what they should do next needs to change their approach.

No matter how much effort, money and time has gone into developing these solutions, your data team need to accept the fact that they are no longer fit for purpose. At best they should be used sparingly but more realistically, put out to pasture. Like any complex system that has worked wonders and saved countless dollars, acknowledging that your baby is no longer beautiful is a hard pill to swallow but it must be done.

The unfortunate truth is that no model on this planet could work in the current coronavirus context. Solutions that rely on analyzing historical company data combined with current figures to inform future trends are going to fail. No algorithm on this planet could accurately predict what will happen purely using the old models. It’s time to give them a graceful retirement.

But there is a solution.

The first of these is by using external data sets…

Instead of focussing on what is happening within your world, change your lens to look elsewhere. Internal intelligence may have been wonderful in the past but now the most reliable form of insight comes from using data outside your company. I would never rely on just one piece of evidence to define strategy but if multiple data points from different data sets all point in the same direction, that gives me the confidence that the trend is accurate. The Golin data stack uses several forms of external sets to help provide insight. These scenarios are:

  1. Listening technology (across social, traditional, print, broadcast and owned)
  2. Visual insights (message clustering)
  3. Audience Intelligence (panels, surveys, aggregate customer data)
  4. Influencer identification (across all channels, target audiences, interests, paid/earned)
  5. Expert intelligence (subscription-based research and market data)
  6. Clickstream data (search trends, web-based traffic, SEO)
  7. Predictive (real-time trends, alerts, crisis/opportunity)
  8. Engagement (CRM, social care)
  9. Artificial intelligence (relevance and sentiment)
  10. Emotional resonance (defining what messaging pulls people towards the brand and which pushes people away)

The second is following the customer journey.

Golin’s customer journey approach encourages teams to focus on business outcomes and perform the right activities to deliver relevant results that achieve them. Industry analyst firm Aberdeen Group have stated that following a customer journey model improves the efficacy of communications by 50%. In other words, you do more with less.

This methodology gives focus that helps determine the optimal approach for gaining success. For example, there is little point making your campaign objective awareness if the main issue your company is facing is that of attitude. An awareness-based program may utilize high-profile influencers and platforms with large reach whereas as an attitude-based project would instead pivot to that of niche, trusted influencers who have smaller but more relevant reach who are respected in the market.

Success metrics are adapted depending on the focus derived from the journey analysis. Instead of picking the same vanilla metrics for every piece of work, the customer journey dictates which KPIs are most suitable for each campaign. This means that share of voice would be a good indicative ROI metric if the focus in the customer journey was ‘consideration’. However, if the customer journey objective was ‘advocacy’ then a suitable metric would be around the volume of customer evidence.

Storytelling variables that give the highest ROI are determined by identifying positioning in the customer journey and then selecting the most appropriate indicative metric that achieves that. This rewards teams for applying a strategy that hits focussed goals rather than those that may not.

However, once again COVID-19 has impacted the model too. Whereas previously many touch points would be face-to-face, these no longer occur. People being influenced by attending events, being in-store, social activities and traditional word-of-mouth are no longer happening. Communications and influence have moved ‘even more’ online. This has two profound impacts:

  • Communications need to adapt to a greater online channel
  • Customer journey modeling needs to incorporate data sets using online methods with greater weight.

The third path to success relies on partnerships.

Partnerships not vendors. The language is important and stresses the fact that partners work on solutions together to get a win-win. I don’t want to work with the largest provider but the firm that wants to co-develop, co-create and win together. Partnerships will drive success and ensuring that relationships are built in that manner ensures that we become stronger as a team.

However, partnerships is not just with external providers but internal teams as well.

If I would have written this a year ago, I would have stressed how the analytics team are the sole owners of data. They are the only ones who can state what is truth and are the single custodians of all insight. However, times change. Just as in the media world where there have been thousands of layoffs and the journalist on the sports beat now writes obituaries and the semi-conductor ‘hack’ has now become all things tech – the time to be precious about data is over. Instead we should allow directional insights to be available to everyone and democratize data. This will allow the now-smaller data teams to focus on the harder strategic analytics whilst enabling greater understanding and utilization of the data function.

The final point as we turn the data world on its head looks at branding. Chief Data Officers are their own worst enemy. Data is merely an ingredient – it’s what you do with it that makes it useful. What does data provide? Insight, strategy, success and profit. Maybe more people would see the value of data if CDO’s renamed their job title to: Chief Talk-To-Me-and-Make-Loads-of-Profit Officer.

Not snappy but we can work on that.

* If you have questions or are seeking counsel, please reach out to Jonny at jbentwood@golin.com.