Predictive models use the latest advances in machine learning algorithms to predict an outcome – whether at an individual level (such as how likely someone is to buy a product), or a macro level (such as likely sales given investment across marketing channels). Our models guide better business decisions by incorporating a variety of different data sources, from surveys and databases to social and API sourced data.
All of our predictive modelling work serves to optimise marketing investment decisions. We can apply predictive models to address broad marketing questions (for example, to understand the effectiveness of different marketing activity) and to address specific marketing challenges (for example, to identify individual customers who are at most risk of switching to a competitor).
Acquiring a new customer is anywhere from 5 to 25 times more expensive than retaining an existing one. So, for any business, keeping hold of the right customers is critical. Our Churn Risk models identify customers most at risk of leaving, allowing pre-emptive, targeted intervention.
We use the latest machine learning algorithms and Natural Language Processing to pinpoint at risk customers. Our models are strengthened by blending a variety of historical data, including customer behaviours, customer touchpoints, customer logs, Net Promoter Score® surveys and more. We can produce a snapshot of churn risk by customer, or embed advanced models in your systems so you can score your customers over time.
Successful marketing reaches the right customers with the right offer through the right channel at the right moment. Propensity models predict consumer behaviour, thereby enabling marketers to steer resource allocation and highlight which customers warrant specific attention. For example, you could reduce direct mail spend by targeting only customers who are most likely to respond. Or identify customers who are more inclined to complain. We build propensity models for your current customers today, and your prospective customers of tomorrow.
Recommendation engines are ubiquitous across the eCommerce landscape, but a small number of transactions is often not enough to understand what an individual may do next. Our learning algorithms help you stay one step ahead of the consumer at all times, and increase your cross-selling conversion. We achieve this by combining the transactional data with as much non-transactional data as possible, such as website engagement.
Survey segmentations based on category attitudes and motivations provide valuable consumer insight but are notoriously difficult to overlay onto a client’s customer database. Doing so provides a powerful mix of knowing how customers are behaving, and why they behave in that way. Our database attribution modelling holds the key.
Tagging customer records with a survey segment codes reveals whether we are attracting the desired customers in the first place, and enables marketers to target customers with tailored propositions and messaging. We maximise our attribution capability by using all available customer data, and enrich this with external third party sources. Merging multiple sources is the best route to maximising attribution.
The saying goes that you should never make predictions, especially about the future. Our view is that, in a marketing context, an accurate forecast of the future is achievable by properly understanding the past. We use historical data to predict future outcomes, and this guides marketing investment and planning decisions.
Forecasting accuracy is optimised by mining as wide a variety of data sources as possible that could influence category behaviour (population statistics, meteorological data, brand tracking KPIs, Google share of search, social media data, to name but a few). Our analysis extends to identifying the specific data sources (or combination of data sources) that produce the most accurate forecasts.
Do we have the right proposition? Is our media mix right for the target audience? Should we divert more resource to print or digital? These are the everyday challenges facing marketers. The reality is that you can’t spend wisely unless you understand marketing’s impact in totality. Our marketing effectiveness and ROI modelling reveals the right approach for your brand and where you will achieve the greatest bang for your marketing buck.
Marketing budget decisions often have competing/conflicting interests. We come at this from an independent and neutral perspective. Our role is to capture the direct and indirect impact of traditional, digital and social media, brand and other factors to guide more objective decisions on marketing spend.