New Ways of Customer Journey-Analysis
June / 2011
1. Introduction The customer journey is currently one of the top themes in the online sector. Many agencies concern themselves with this topic because understanding the customerâ€˜s journey enables targeting of advertising within specific channels. This attracts the attention of target groups wherever they happen to be at a given moment. Customer Journey Analysis is a popular method of determining how the marketing budget will be most efficiently distributed, so that sales may increase and advertising costs may be reduced in the long term. To determine which channels customers encounter a product, agencies routinely employ efficient technical tools to uncover user paths. However, analysis of the customer journey through technical solutions is limited by certain restrictions which, in the worst cases, render the tracing of customer paths impossible. The risks associated with this kind of analysis have led QUISMA to research alternative solutions. It was concluded that so-called â€˜modelingâ€™ is the best method for targeting. This approach, originating in classic media planning, makes it possible to determine the extent to which individual channels contribute to the generation of sales. Murat Cavus, the senior manager of Marketing Intelligence at QUISMA, below explains the goals of Customer Journey Analysis and the restrictions to which tracking technologies are subject. Also to be introduced is the crosschannel optimization which enables targeted budget allocation based on the modeling.
2. The Boundaries of Classic Customer Journey Analysis
Customer Journey Analysis describes the path of the customer from the first point of contact with the product through the completion of a transaction. Knowing which paths customers typically follow online enables the company to concentrate their advertising efforts in the channels most likely to be used by the target group. Advertisers who are familiar with most of their usersâ€˜ path have a great advantage; namely, they can tailor their efforts to their customer portfolio and know which combinations are most likely to succeed. This knowledge enables them to efficiently manage their budgets and make decisions to invest in certain advertising channels based exclusively on their likelihood of succeeding. The success is later reflected in higher sales as well as lower marketing ratios and costs-per-order. All agencies in the online sector employ technical solutions to model their customersâ€˜ journeys. The customer paths are modeled through tracking systems. However this presents problems for budget allocation from the outset: firstly, this method contributes to a successful outcome only when advertisers communicate exclusively through internet and manage and oversee all efforts through a single tracking system. Apart from the limitation to a single medium, problems also arise which hamper the modeling of the customer path and thus necessarily lead to distortions. Companies and agencies should thus consider the following restrictions when making budgetary decisions concerning the management of advertising campaigns through tracking systems:
Device-change cannot be traced by tracking systems Device-change is not taken into consideration by technical tracking solutions. However most users today use several terminals (mobile phones, laptops, tablet PCs etc.) to gain information about products online. For example, a user at his office PC might become interested in a particular product and research it later on his home PC, but not before gaining some more information over his smartphone, and then conclude the transaction on the computer at work. This path cannot be reproduced by tracking systems because they only collect individual unique IDs. This means that the user is assigned a separate unique-ID at every terminal. In this example, the system would identify only the first and last clicks with the same user. The customerâ€˜s path thus cannot be completely represented and understood. Because of this limitation, the representation is automatically marred by errors. The contributions of individual channels are left unmapped because some user-clicks and thus the complete path are left untraced. The customer journey is riddled with gaps, which hampers if not renders impossible the optimal allocation of budgets.
Cookie-deletions are left unconsidered A similar problem arises with users who regularly delete their cookies. Deletions give rise to distortions in the customer journey because the customer path cannot be fully mapped. Only the activities following the deletion are identifiable. Should the customer take notice of a product before deleting cookies, this point of contact is lost.
External factors cannot be taken into account by the calculations External factors such as offline advertising are not taken into consideration by tracking systems. Points of contact with the user through TV-, newspaper, and periodical advertising cannot be represented by online tracking systems. A further complication is that special offers or seasonal fluctuations are left unconsidered as well.
Use of different tracking systems by various providers Should businesses employ different providers to track their online advertising activity, the customer journey can only be partially traced. If, for example, provider-A tracks the display and affiliate activities while provider-B monitors SEA- and SEO-activities, then each provider can only coherently represent the areas for which they are responsible. A holistic consultation concerning optimal budget allocation is not possible in this case. This restriction also leads to a distorted outcome.
QUISMA also currently provides Customer Journey Analysis over tracking systems. However, because of the limitations described above, we have searched for an alternative solution, which we have found in modeling. Modeling is free of the problems described above and facilitates an accurate representation of the customer journey, considering all relevant factors.
3. Modeling The modeling solution entails a multivariate analysis in which many factors, i.e. sales, advertising, seasonal effects, price, etc. are considered in the context of their causal relationships. The goal of assembling this data is to analyze the return on investment of factors with respect to achieved sales, from which recommendations for an optimal allocation of the budget can be extrapolated. During the modeling, the value contributions of each channel to the generation of sales is calculated. Individual paths are not reproduced. Rather, the causal relationships and value contributions of each channel and of each external factor are calculated. The so-called QUISMA modeling 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. Modeling builds a neutral foundation for an optimal allocation of the budget since it determines the relationship between actual sales and the totality of 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 modeling over 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. The following representation is an example of which factors can be included in the modeling:
Example of which factors can be included in the modeling
Seasonal effects Competition
For example, online advertising can be search engine advertising, search engine optimization, display advertising, or affiliate marketing. Additionally, activities in the conversion optimization sector can be integrated into the modeling because of their positive effect on sales. Conventional advertising activity is subsumed mainly under the category of print and TV, which is usually included in the evaluation 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. Modeling enables the extrapolation of prognoses concerning 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 modelâ€˜s calculations. The sales process can be represented with the data. Based on the model, it is possible to identify the effects of individual changes on sales. As a result, the advertising budget for future efforts can be associated with the earningsmaximization goal. Advertising companies can thus use modeling as a tool to better plan future marketing and investment decisions. Modeling enables better strategic decisions since it reveals complex interrelationships which cannot be by discovered through simple descriptive analyses.
The classic sector often resorts to modeling since it provides strategic management a better foundation for decisions than do conventional tools and solutions. QUISMA is among the first agencies to use modeling in the online sector so that investment-intensive decisions can be better planned and structured than was until now possible. To sum them up, modeling answers the following questions:
• How high is the return on investment of my online and offline • • •
marketing activities? Which of my advertising channels present the greatest potential for growth? How do I optimally allocate my advertising budget among the various advertising media? How does classic advertising affect buying behavior in my online shop?
4. Regression analysis Modeling is based on the econometric model of regression analysis, which serves as a foundation for all action recommendations and delivers realistic forecasts to advertisers. Regression analysis is itself one of the most often used multivariate analyses. This method examines the dependence 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 analyses. Moreover, forecasts for future development can be extrapolated through this method of analysis. Regression analysis is of great use in economic sciences, providing answers for all sorts of problems, for example:
• 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
Regression analysis is thus especially advantageous because individual dependent variables can simultaneously be juxtaposed with one or more independent variables. Thus a holistic explanation and a better description of the dependent variables‘ trajectory can be made available. In the case of modeling, a relationship is established between existing sales and advertising activity. Sales are thereby modeled as the result of advertising activity. 7
A linear regression analysis is employed to assess sales within the framework of modeling. The linear regression analysis supposes a linear relationship between the dependent variable, which is scaled metrically, and one or more independent variables.
The regression function proceeds as follows:
(1) y = b + ∑ b * x + e i 0 k∈K k k,i i
Value of the i-th observation for the dependent variable, i.e. sales
x(k,i): Value of the i-th observation for the k-th independent variable, i.e. clicks on SEA Adwords or clicks on display banners b0:
Constant of the regression function, i.e. sales, without advertising services
Regression coefficient for the mapping of the k-th independent variable‘s influence, i.e. the degree of influence of SEA Adwords campaigns on total performance (sales)
Residuum of the i-th observation
Index sets of the observations Index sets of the variables
To conduct a regression analysis, the values of the dependent variable Yi as well as of the independent variables Xk, i must be available. Without these values a regression analysis is impossible. All other values, like the constant of the regression function b0, the regression coefficients bk (k∈K), and the residuals ei (i∈l) are estimated within the framework of the regression analysis. Complex mathematical algorithms are used to estimate the regression function. These methods entail huge computational efforts. Thus powerful statistical programs are often employed to determine the functional relationship. Should the data be available, it can be read into the program and analyzed. The relationships can finally be extrapolated and interpreted from the results of the regression analysis. However the challenge that regression analysis poses lies not in the estimation of the function (which the program does automatically) or the interpretation of the results, but in all of the preliminary work. This includes:
• The establishment of hypotheses regarding functional relationships • The selection of an approximation method appropriate to the • •
established hypotheses Data collection Data preparation in which data are adapted for use by the method selected
A great amount of time should be allotted for these activities. However, if the approach is carefully thought through and carried out, the regression function can be unproblematically estimated and interpreted. Using the interpretations, recommendations can be formulated based on fixed mathematical facts which can be neither identified nor extrapolated from conventional analyses. In the following chapter is a case study of how modeling achieved an increase in sales and a decrease in the marketing ratio for a client.
6. Empirical Evidence QUISMA has been supporting all the online activity of a leading German fashion store since the beginning of 2009. The services include year-round measures to increase awareness and sales as well as complete media planning, including optimal management of the marketing budget. QUISMA used modeling, namely regression analysis, to manage the budget. Sales were modeled as the result of customer activity. Investments were used for activities in the booked channels. These were affiliate marketing, display advertising, retargeting, search engine advertising, and search engine optimization. To assure optimal management of the budget, QUISMA evaluated the performance of the individual channels as well as the influence of the channels on the overall performance. These procedures made it possible to consider complete user activity and recognize cross-medial effects between the channels when allocating the budget. Taking the cross-medial effects into consideration, a modeling was first calculated for the timeframe of January 2009 to June 2010, on the basis of which the influences of the channels on the total performance have been determined. Based on these results, a budget of nearly the same size was distributed anew for the last two quarters of 2010 and then compared with the last two quarters of 2009. Budget allocation in the initial situation: Affiliate Marketing
Budget allocation in the initial situation in Q3-Q4 2009 Source: QUISMA
In the business year 2009, half of the budget was invested in the SEA sector. Investments were made above all in generic keywords, with the goal of increasing awareness. Other advertising channels were thus considered only secondarily. The emphasis on the SEA channel led to an increasingly worse marketing ratio. Budget allocation based on the modeling: Affiliate Marketing
12% Budget allocation based on the modeling in Q3-Q4 2010 Source: QUISMA
The Modeling revealed that the display advertising channel initiated sales in other channels, among which retargeting, SEA and SEO especially benefited. Through the cross-over effects identified with the aid of the modeling, the budget was redistributed in favor of display advertising and the SEA budget significantly lowered. The results achieved through modeling are remarkable. Although the budgets were lowered in all channels except display advertising (which significantly increased) and SEO (which marginally increased) the sales achieved barely dropped, if at all. In contrast, display advertising initiated sales in the SEA, SEO and retargeting channels.
10.000 7.500 5.000 2.500 0
Financial year (Q3-Q4) 2009 Source: QUISMA
10.000 7.500 5.000 2.500 0
Financial year (Q3-Q4) 2010 Source: QUISMA
A comparison of the last two quarters of 2009 and 2010 reveals that sales increased by 24% with a nearly equal budget, while the marketing ratio fell by 43%.
Sales increase Source: QUISMA
0 Sales 2010 Q3-Q4
Sales 2009 Q3-Q4
7.50% Marketing ratio decrease Source: QUISMA
0 MR 2009 Q3-Q4
MR 2010 Q3-Q4
7. Conclusion An efficient allocation of the advertising budget among different online channels is essential, above all for clients who invest high amounts in online advertising. Allocation based on technological solutions alone has proved to be sub-optimal, since many businesses who devote large budgets to online advertising also invest in offline advertising (TV and print). Technological solutions would overlook offline advertising completely in this case. Additionally, restrictions like device-change, cookie deletion, and tracking by various providers lead to erroneous conclusions regarding the actual customer journey. Because of these limitations, an efficient allocation of the budget among various advertising media is rendered problematic. In contrast to the aforementioned method, modeling calculates the contribution of every single channel to the generation of sales. Individual paths are not reproduced. Rather, causal relationships and valuecontributions of each individual channel (online and offline) and of each external factor are calculated. Modeling provides a neutral basis for an optimal allocation of the budget, since actual sales and the entire body of data collected about advertising activity are situated in a direct causal relationship. The result is an exact representation of the value contributions of each activity to the sales generated. However, the greatest advantage of modeling over tracking systems is that offline activities (TV, print) as well as external factors (seasonal effects, competitive activity, market trends, prices, etc.) can be included in the sales-generating process.
Thus it is clear that: â€œModeling is the next level of customer journey analysis.â€œ
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