By guest blogger Katharine Hulls, VP Marketing at Celebrus Technologies
With digital marketing reaching new levels of maturity, the pressure is now on to demonstrate the role each channel and activity plays in driving business value and optimising investments across digital channels.
How therefore are organisations planning to identify potentially wasteful affiliate spend, reduce PPC and SEO costs without impacting results, or identify which activities most contribute to different online transaction types – and when?
These essential measures are certainly not going to be achieved using traditional aggregate-level data or ‘last-click’ analysis. Katharine Hulls, VP Marketing, Celebrus Technologies, outlines the importance of individual-level data in developing the insight needed to build robust, accurate digital marketing attribution models which take into account the customer’s whole route to conversion, not just the first or last click.
Justifying Optimism
Marketing optimism may be on the up according to the latest Bellwether report, but unless companies can evaluate marketing effectiveness and demonstrate return on investment, that optimism may be short lived.
With budgets increasing for the first time in years, marketers can begin to exploit omni-channel marketing opportunities and attain new levels of customer insight. However, there is no return to the blank cheques of the past: digital is still highly compelling but justifying spend and demonstrating value for money is now, quite rightly, top of the agenda. It is essential therefore that organisations move beyond current, somewhat blunt, models for measuring attribution and achieve accurate and credible insight into performance.
Critically, it is time to admit that attributing the full value of the marketing investment to the last or “converting” click is fundamentally flawed. By ignoring all previous activities which have contributed to the eventual sale, this approach is inaccurate, untrustworthy and often results in investment in key activities being reduced or cut.
Complete Path
‘Last click’ misses essential elements of the consumer journey. With even the fastest moving products, there are usually at least a few stages in the buying cycle from initial awareness through to eventual purchase. ‘Show rooming’ is a prime example – with customers increasingly comparing prices and reviews on a mobile device whilst in store and about to make a purchase. Indeed, according to the latest US figures from Pew Research Centre, 25% of mobile users look up prices online and 24% look up product reviews whilst in store.
It is therefore essential to build a complete and detailed view of the way in which customers interact with the organisation, including digital media and messages and potentially at least one social media, to understand the role each element plays within the buying cycle. This can only be achieved with individual-level data from the online channels, including websites, mobile apps and social media.
Without this insight, marketers simply cannot accurately or confidently answer the critical questions that will determine effective investment and ongoing strategy. For example, are specific PPC terms designed to improve customer acquisition driving new visitors to the site or simply delivering brand aware users who would visit anyway at much lower cost? Will an increased investment in those paid search terms that drive 10% of site traffic, at the expense of display advertising, impact overall results? Are affiliate payments based on their true impact on the business or not fairly rewarding those that deliver new prospects because the conversion rate of the visitors they drive is low based on last click analysis?
Building the Model
To build a full customer journey attribution model requires not just the current interaction between customer and brand online, but previous interactions as well. This data, combined with inherent business knowledge, enables a company to build a picture of each phase of the buying cycle and the relative role and importance of each interaction. In addition, it empowers organisations with long and complex sales cycles to understand and replicate the most effective and efficient funnels in the future.
With an understanding of the timing and shape of the customer lifecycle, an organisation can decide how far back to look to understand the influence of activities on the eventual sales – a key decision in the development of the attribution model. For example, an insurance company might decide that customer interactions within the previous eight months should be included in their attribution model, whereas an online health food store might only look at the last two.
Another factor to consider is the proportion of influence, or percentage of the sale value, or even lifetime customer value, that should be allocated to each interaction, from first click to last. Some organisations will decide to allocate the influence evenly across all touches, particularly if sales cycles are short or to simplify the initial process of understanding attribution. Others will opt to give a higher weighting to touches in the second half of the sales cycle since they are closer to the converting event; or vary weightings of different visits based on the number of pages seen during each, or in more sophisticated models, the types of pages viewed during each visit.
Conclusion
Building an accurate attribution model is not a one-off; it is a long-term, iterative process. But it must increasingly become a fundamental component of the marketing function; a strong attribution model is now essential to evaluate market effectiveness and ascertain the direction of ongoing spend. With individual-level online data, the marketing team can confidently justify spending decisions based on the proven value of each marketing activity and optimise investment across the digital channels.
Furthermore, attribution data has a role to play in the broader marketing strategy: it can be combined with existing customer intelligence data to build a single customer view, enabling accurate profiling that significantly improves segmentation and targeting. In addition, by segmenting customers according to criteria such as profitability and lifetime customer value, it is possible to measure differences in customer journey and attribution models by customer value to ascertain whether certain activities build more or fewer profitable customers.
This accurate campaign attribution information offers the potential to transform the way digital marketing is conducted. Incorporating individual-level campaign attribution information into decision making will optimise marketing activity and investment and truly justify marketing optimism.