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Boundaries of 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 helps 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 users’ 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 advertising channels based exclusively on their likelihood of succeeding. The success is reflected in higher sales as well as lower marketing ratios and costs-per-order.

Possible factors considered in a sales modelling

Possible factors considered in a sales modelling

All agencies in the online sector employ technical solutions to model their customers’ journey. 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 form 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 therefore 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. Tracking systems cannot reproduce this path, because the systems can only collect the 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 the last clicks with the same user. The customer’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 as well leads to a distorted result and prevents a realistic representation of the customer journey.

Due to all these restrictions alternative approaches will have to be found. We from QUISMA see sales modeling as such an approach. We are the only online agency, which uses the customer journey analysis. This approach is free of the above-mentioned problems such as device-change, cookie deletions, tracking via different providers or external factors. During the modeling the value contributions of each channel to the generation of sales is artificially extrapolated. Individual paths are not reproduced. Rather, the causal relationships and value contributions of each channel and each external factor are calculated. The so-called QUISMA sales 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 the 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 exact approach of modeling can be read in our blog entries about sales modeling (here, here, and here). In the near future we will also have a Whitepaper available for download on the QUISMA website, where we will explain this approach in detail.

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