Andrew Addison, CEO of Purple Square, explains how predictive analytics can help companies communicate efficiently during the cost of living crisis.
There’s no sugar coating the fact that times are tough for everyone at the moment. Rising costs, interest rates and inflation mean consumers are spending less and businesses are having to work harder than ever to stay in the black. For some, a knee-jerk reaction is to cut out spend on areas like marketing to help save the pennies, but more often than not this turns out to be a false economy. Cutting costs on marketing means decreasing your visibility, something businesses can ill afford.
In general, there is a greater understanding of the need to spend more wisely when times are tough, rather than eliminating services like marketing altogether. Ninety percent of the conversations I have with customers at the moment are about how to make spend go further and doing more with less. With marketing, this translates into how to prioritise which people to communicate to, about which products or services you’re offering and, arguably most importantly, which channels are the most effective in getting through to them.
Technological solutions, such as predictive analytics can be a powerful tool to help ensure businesses are looking at the right mix of channels to ensure they get most out of their marketing spend. Still seen as a ‘dark art’ by some, predictive analytics is anything but. Here are some tips to help organisations get it right:
It is okay to not talk to, and potentially lose, customers. This may seem counter-intuitive to doing business, especially at a time where every pound counts, but fundamental to marketing success is to be comfortable with prioritising the customers you want to grow, over the lower value ones. If a customer always buys your basic product, for example, marketing to them may mean you are cannibalising opportunities to win new customers. Contacting everyone in your database is prohibitively expensive, which means you need to be brave enough to cut them loose – at least when it comes to marketing spend.
Why waste your precious resource on ‘preaching to the choir’ as it were? Predictive analytics can help you to know where to focus your marketing spend on customers that can bring the most value.
Look at the big picture. As with any data science application when used in isolation, predictive analytics can end up doing more harm than good. For example, an individual product manager may work with the CRM or data science team to target the best opportunities for their individual product lines. This narrow approach can result in going down a rabbit hole of data, skewing the importance of a single channel suitable to a specific campaign or audience while unintentionally diminishing ones that may be more profitable to the business as a whole.
Taking a step back and looking at the bigger picture is essential with predictive analytics. The overall business operational marketing success needs to be prioritised over that of a single product or campaign.
Customer viewpoint, C-level ownership. One of the biggest challenges businesses have with predictive analytics is who owns it. To prevent the example above from occurring time and again, predictive analytics needs to be driven from the overall customer journey or experience team, not a single product manager viewpoint.
The CMO or Head of Customer Experience backed by marketing operations experts is the ideal team to take the lead on predictive analytics – incorporating the granular level of detail with the wider perspective required for big picture thinking when it comes to predictive analytics. Together they can bring the data together and identify, prioritise and rank it against specific offers, channels and audiences to get a valid output. From there, they can prioritise the channels, audiences and campaigns that will bring the most value to the business as a whole.
On-demand modelling. As with all things marketing and technology, balance is essential. Achieving the right balance of granularity with high level perspective is as important as knowing when and how to use modelling. Running the same model every day for weeks may yield more data, but is overkill and essentially creates data for the sake of it. Instead, marketers can use on demand modelling to gather real data at the point in time when they need it within the context of a campaign.
Perhaps the most important tip to remember when it comes to predictive analytics is not to fear it. It is not there to bamboozle, but to assist. When used alongside marketing automation by the right team to help create a business marketing strategy, predictive analytics ultimately help arm organisations with the information they need to increase the value of their marketing efforts and to make better decisions long term.
