Understanding the customer journey has always been crucial to determining the most effective use of advertising.
While there are many technical solutions out there which help uncover the path to conversion, particularly within the online sphere, the incorporation of more traditional methods such as modelling are proving successful in providing insights not just for online marketing decisions but importantly for multichannel analysis.
Here are five considerations for getting the most of your customer journey analysis...
1. Know the limitations of the tech
Since many businesses who devote large budgets to online advertising also invest in offline advertising (TV and print), allocation of budget based on purely technological solutions can actually be sub-optimal because it overlooks offline advertising completely.
Additionally, restrictions like device-change, cookie deletion and tracking by various different providers can lead to further erroneous conclusions regarding the actual customer journey.
Because of these limitations, efficient allocation of budget across various advertising media can be ineffective.
2. Adopt sales modelling
Sales modelling is a multivariate analysis in which the causal relationship between sales and influential factors (i.e. online advertising, offline advertising, and external factors) is determined.
Modelling builds a neutral foundation for an optimal allocation of the budget since it determines the relationship between actual sales and the data collected about advertising activity.
The result is an exact representation of the value contributions of each individual activity to the generated sales. However, the greatest advantage of modelling above tracking systems is that offline activities (TV, print) as well as external factors (seasonal effects, competition, market trends, prices, etc.) can be included in the sales-generating process.
Modelling allows for better strategic decisions since it reveals complex interrelationships which cannot be discovered through simple descriptive analyses.
3. Integrate conversion optimisation into sales modelling
Conventional advertising activity primarily includes print and TV, which is usually evaluated in the form of GRPs. In addition to advertising activity, external factors such as price-management, seasonal effects, macroeconomic factors, weather, public relations, etc. may also be included.
Modelling enables the forecasting of future success based on past data. After the influence of individual factors on sales is predicted, the data can be projected into the future based on the model‘s calculations. The sales process can be represented with the data.
Based on the model, it is then possible to identify the effects of individual changes on sales.
4. Implement regression analysis
Regression analysis is itself one of the most often used multivariate forms of analysis. This method examines the relationship between a dependent variable and one or more independent variables.
With the help of a regression analysis, relationships can be discovered which are left ignored by simple data analysis.
Moreover, forecasts for future development can be extrapolated through this method of analysis. Regression analysis can provide answers for all sorts of problems including:
- Estimating the dependence of the quantity of sales of a product on the preferences of specific target groups.
- Estimating the dependence of the quantity of sales of a product on the price level.
- Estimating the dependence of the quantity of sales on the advertising budget, price, and field of operation.
5. Use regression analysis to assess sales within your framework
Regression analysis is especially advantageous because individual dependent variables can simultaneously be compared with one or more independent variables. Thus a more complete explanation and a better description of the dependent variables’ trajectories can be made available.
In the case of modelling, a relationship is established between existing sales and advertising activity, with sales modelled as the result of advertising activity. A great amount of time should therefore be allotted for these activities.
If the approach is carefully thought through and carried out, the regression function can prove time efficient and highly successful at delivering insights which would not be available through conventional analysis.