In the age of big data there’s no shortage of stats and figures, and more often than not these confuse more than clarify. But that needn’t be the case, now that linkage analysis combines data sets to extract more tangible and actionable insights.
Take advertising for example – every advertisement brings with it its own set of data and key performance indicators (KPIs) to judge it by, but what success means is different for every business. For data to prove itself useful, it must be transformed into real financial and business metrics.
To do this, linkage analysis combines different sources of data to uncover important relationships between variables. By combining survey data and client data, linkage analysis can demonstrate how an advertisement affects a brand in terms of measures such as brand recognition, consideration, customer satisfaction, business performance, ROI, etc. For instance, if a fast food chain wanted to know how their most recent ad campaign had affected the sale of cheeseburgers they might use linkage analysis to compare sales figures with brand recognition in their ads.
Statistical modelling can then be used to explore the relationships between tracker data and organisational performance KPIs, to develop an integrated picture of the drivers of business performance so that clients can focus on the areas that have the greatest ROI.
This kind of predictive analysis poses the question: if you increase X then what happens to Y? Or, returning to our fast food chain, if you increase investment in an advertisement by X thousand dollars then Y percent more people will recognise your burger on their walk down the street. These predictive models allow clients to learn the optimum moment where they can achieve the most gain for the least possible expense.
Do you want fries with that analysis?
So, back to the fast food, supposing you own a fast food restaurant and you have decided to do some linkage modelling to investigate how different aspects of your business work together, such as ad spend and brand recognition or sales figures.
Two important graphs help us to understand the relationship between KPIs of interest: the scatter plot and the quartile chart. Our example graphs illustrate a strong positive correlation between fast food brand recognition and investment in each advertisement. At a glance, it demonstrates that advertisements with higher investments tend to experience higher levels of brand recognition.
But does it change things?
We saw above that the correlation analysis revealed a connection between ad spend and brand recognition, but correlation does not always denote causality. This is where predictive modelling adds additional value to this analysis.
Predictive modelling analysis can quantify these relationships so that researchers can not only interpret if there is a relationship between factors X and Y, but how much. For instance, if the fast food chain spends $1,000 more on advertising then they can expect Y percent more recognition. Quantifying these relationships allows researchers to find the optimum moment between factors to provide effective results.
Creating a story behind the data which drives strategic investment decision-making
With the use of linkage modelling, it is possible to understand and quantify the relationships between different KPIs from different data sources. The insights inform marketers about where to invest their efforts to stand the best chance of delivering high performance.
Every data set has a hidden story, and rather than delivering raw data that provides few tangible insights, linkage modelling allows a narrative to be drawn from the data, interpreted to deliver tailored and actionable insights, revealing the stories behind the data rather than just a page of figures.
Actionable insights are the key to unlocking the potential of data and using it to drive performance. Linkage modelling is the ideal solution for extracting the story behind the data quickly and cost-effectively.
- To learn more, contact Jonathan Dodd at firstname.lastname@example.org ph 021 538 634