Be Agile

Marketers are under more pressure than ever to deliver results. But the combination of a seemingly never-ending wall of martech, shrinking budgets and finding the right resource to deliver on this – it’s a helluva minefield.   

Prior to, during and more so as we cruise out of the pandemic –brands that are seeing success have moved from a pure campaign approach to a cycle of continuous experiment, testing business outcomes and optimising.   

This is classed as ‘agile marketing’.  A marketing approach in which marketing teams identify high-value projects, and focus their efforts in sprints.  These projects are measured, the data analysed and action taken which incrementally improves results over time.  This way of operating is ensuring that validated learning, with data, overrides opinion and helps overcome silos. 

The European Double up

This view was reinforced to me, whilst reading the Accenture sustainability paper, the European Double up. CEOs were surveyed on pandemic-related challenges, specifically about the intersection of digital technologies and sustainability.  When asked about the pace of recovery, post-pandemic, it was clear that companies emerging with the strongest potential for profitable future growth had not lost any ground in terms of agility, since the onset of the pandemic, thanks to their agility pre-pandemic. 

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This isn’t just theoretical.  We have worked with brands during, and now post pandemic.  And some of the findings, have and continue to have a significant impact, not only on our clients’ sales and revenue figures but customer conversion rates have dramatically improved.  Frankly it’s been staggering to watch the results come in.  Here are a few examples: 

The brief: 

Our current understanding of onsite behaviour, lifetime value, journey trigger points, as well as product mix range and combination are limited.  There are key questions that need unlocking to support our trading plans and portfolio strategy to help shape plans.” 

If we unpack that brief. The question really is we don’t understand how & why, which customer types are purchasing which products.  Help us stop guessing so we can plan for the future.  

This particular client saw a huge surge in demand during the pandemic.  It would be very easy to cling on to the tails of success.  Instead, the smart senior team took the opportunity to pause and analyse to experiment and optimise.   

This analysis was carried out in Adobe Analytics using features such as dimensions, calculated metrics, classifications, segments, and reports.  New workspaces were created – lots of which enabled comparison of web vs app data.  From our analysis, we were able to make recommendations such as:  

  • The optimal product mix for specific customer journeys. 
  • Having identified significant differences across different user groups with different user frequency, different sales messages should be tested with these different cohorts. 
  • Focusing on some basic information such as what is the source of incoming traffic and where do other channels fail to drive activity? We made recommendations on the messages delivered to different customer groups through those channels at key times.  
  • The type of content which drives better engagement. Thinking about what messages should be delivered to which groups, at different stages of their lifecycle has a significant impact on results.  Some big learnings were taken from this analysis especially between web and the app. 
  • How ways to pay often contributed negatively to conversions. 
  • Sales events were a massive factor in driving cross sale opportunity, and customers acquisition strategies needed better consideration alongside these events.  

In another example, working with a client at the end of 2020, we noticed that over time their email frequency to their database had increased significantly and as the frequency grew, the open rates had dropped dramatically.  Our view is you can create too much content.  Inboxes are overwhelmed.  Cut back, personalise to your customer groups and send out emails with meaningful content.   

We encouraged the client to reduce email communications, which they did by a whopping 92%.  The result was immediate and impactful – open rates shot up whilst conversion rates increased to percentages never achieved before, and further analysis show us deeper and longer engagement with the site across the board.  

Another Yard client asked us to analyse both their car and home insurance products.  We focused on path, journey, and cohort analysis.   For example, comparing visitors from different sources, think direct vs aggregator delivered visits: 

  • % of visits complete transaction 
  • % of drop outs, at what stage 

Our journey recommendations included: 

  • Removing mandatory info such as email/phone when receiving a quote – users drop out at an alarming rate. Users simply aren’t ready to share their personal details pre-quote! 
  • Personalised content across the insurance age brackets cohorts. For example, certain generations add different extras at different times of their journey. 
  • Progress tracker, such as time-based indicators so users know how long an application process takes. 

Understanding the challenges at each of the pivotal points, and personalising wherever possible, makes the difference to 000s of applications. 

Just 3 client examples where analysing the data can make a huge difference to your customers and your trading revenues by doing much more with the attributes you already have. 

So, what should your organisation be doing?  

Be brave.  If you’re doing the same as you’ve always done it’s unlikely you’ll get better outcomes. We’re all agreed on that, right? 

Adopt a trading approach. Sector leaders take a trading approach to data. Think, how are critical questions being asked in scrutiny of the numbers?  

Embrace agility. Is your organisation set up to respond to recommendations, in an agile fashion? Is your senior team flexible? Are they encouraging engagement in experiments which analyse the ever-growing data streams that will enable valuable customer discovery over static performance predictions?  This is how leading businesses keep up with the market and shifts in consumer behaviour. 

Ask for help. Get in touch with us here at Yard.  We would love to have a chat and see how we can help you get even better return from your data. My email is

Collette Easton