This blog post is an excerpt from our white paper; How to Build a Data Driven Marketing Organisation: A guide for CMO's and Marketing Leaders. In it we discuss how data, analytics and insights are truly transforming the way marketing departments are doing business. We discuss three key areas:
It is our intention that once complete, marketing leaders will have a further level of insight on how data and analytics can help shape their marketing, how business intelligence can help, as well as a roadmap for starting.
You can download the full paper here.
In this post, we look at how companies are using analytics in the attraction phase of their marketing and how that flows into the selling phase. This includes marketing mix modelling, hyper-personalisation and customer lifetime value.
Marketing Mix Modelling (MMM): With any modern-day approach to marketing, understanding closed loop ROI across programs and channels is the holy grail for quantifying marketing effectiveness. MMM uses the principle of multi-linear regression, a statistical technique that determines the linear and non-linear relationship between marketing efforts and subsequent sales. The dependant variable is sales while the independent variables are the programs. Companies aim to quantify the effect of marketing on revenue by understanding the impact of each variable on each other.
Once the historical effectiveness is established and split out from a macro through to channel level (a win within itself), predictive models are applied to forecast the effectiveness of future efforts. This manifests itself with a view on what programs should be run at what time and to who. This in turn allows marketers to understand what programs they should be focusing on and where budget allocation should be based upon impact to sales and revenue.
Hyper-Personalisation: Salesforce found that 52% of customers are extremely or somewhat likely to switch brands if a company does not make an effort to personalise communications with them. AI and machine learning-based marketing tools are changing the very core of how marketers make decisions and deploy campaigns to achieve hyper-personalisation.
Hyper-personalisation is being enabled via understanding behaviour of an individual rather than a statically defined assumption. AI-based tools are making personalisation easier by learning through each interaction and delivering an aligned experience based upon those interactions.
From a programmatic standpoint, one of the biggest challenges marketers have when trying to personalise interactions with their audience, is understanding and developing the myriad of content and subsequent combinations required to achieve personalisation. New age AI based systems can now process marketers pre-define rules and directions that create and deliver individualised content, in real time to each recipient.
Customer Lifetime Value: If Pareto’s principle (the 80/20 rule) still rings true for businesses, the identification of the customers who bring us the most value and nurturing them should be a priority for all marketers. The goal of predictive customer lifetime value is to model the purchasing behaviour of customers to predict what their future actions will be.
Predictive analytics can help marketers identify visitors and existing customers who are more likely going to be high-value contacts or long-term customers. This can start with developing customer value classes based upon segmentations and purchasing behaviour. Once established, different rules and effort levels can be applied based upon the predicted value that this customer will bring to the organisation.
These patterns can also be further analysed and pressed upon as more data is presented. The more in-depth understanding we have about our customer’s behaviour, the better we can design our products and optimise promotions for these customers resulting in greater revenue.