Businesses are constantly striving for new insight and to maximise the ROI from customer data gathered across multiple digital channels, according to Katharine Hulls, vice president for big data software firm Celebrus Technologies, who explains here how this data - and the ways it can improve business performance - is evolving.
Web analytics and database marketing were once used independently of each other, but the two can now be brought together to provide powerful insights and value to an organisation's marketing efforts. Hulls suggests that there is considerable business value to be gained by merging traditional database marketing techniques and digital analytics data, highlighting examples of how to bring together these disciplines and feed a digital database marketing team with streams of highly granular online data.
Historically, the disciplines of web analytics and database marketing were individually regarded as highly valuable, but each approached their role from a different perspective. Web analytics focused on developing an understanding of interactions with a website - for example identifying the products most clicked on and key drop-off points. But web analytics has recently matured into digital analytics to account for the proliferation of multiple digital channels. Nonetheless, the key tenet of understanding overall behaviour and trends remains.
In comparison, database marketing has evolved over many years into a vast marketing discipline, using a raft of well-proven methodologies to explore data and generate insight. The insight which is unveiled can be aggregated, segmented and drilled down into, but is essentially based on creating an understanding of individual customers by enabling patterns to be recognised, explored and understood.
Both digital analytics and database marketing still add considerable insight and value to an organisation's overall marketing strategy, however, the challenges today's marketers face cannot be solved by viewing these two as distinct disciplines. Increasing website conversion rates or driving highly targeted email campaigns is no longer enough. Marketers must now be able to understand their individual customers across all channels in order to engage and entice them in the manner each customer chooses.
The ability to understand individual customers' behaviours across digital channels has previously been a stumbling block for organisations as the data has not had the level of detail required to do so. Nor has it been available in real-time to drive actions such as real-time website personalisation. But this is changing with real-time streams of individual-level data now available to feed into customer analytics and engagement programmes.
The issue is now often having the people with the right skills, experience and mind-sets to turn that data into better marketing decisions and actions. Those people are not commonplace; however, there is a route which an increasing number of companies are taking that involves combining the best techniques, data and ideas from both digital analytics and database marketing into digital database marketing.
We are beginning to see some forward thinking companies executing powerful digital database marketing programmes that drive considerable business value. To do so, however, requires the availability of data to identify individual customer interactions across multiple digital channels, especially websites, mobile apps and increasingly social media. Once this data is available within a business in a usable, accessible format, it can drive a variety of programmes that can increase revenue, heighten brand loyalty and maximise marketing effectiveness.
The following four points highlight how the benefits of digital database marketing can best be realised:
- Increasing revenue through personalised communications
It is well known that people interact and engage with communications which are meaningful to them. Organisations know that leveraging purchase data and product recommendation will help to increase the relevance of communications through promoting specific products that are likely to be of interest to the individual.
This is made even more powerful by using data about what the individual is interested in buying. And while basic remarketing is well understood, personalisation and remarketing can extend well beyond this basic level by using product browsing behaviour, brand affinity, product association data and even factors such as a customers' colour preference - it can all aide the personal viewing and remarketing experience.
This type of highly granular data can be fed into customer segmentation and individual targeting techniques. Organisations can now add to current tactics, such as using complimentary images of previously browsed products in promotional emails, to personalise website content in real-time as the customer moves around the site. This greatly increases the relevance of communications and therefore the impact.
- Marketing effectiveness through lifecycle attribution modelling
Understanding an individual's interactions with a brand over time requires the building of attribution models which take into account the full customer lifecycle so that a brand can sees what is relevant for the sales cycle. An organisation armed with data about a customer's interactions across multiple channels can develop attribution models to assign value to each marketing activity which drove customer interaction. From this data they can assess the relative importance and role of each marketing action and optimise budget allocations accordingly.
- Marketing investment and predictive campaign optimisation
It is not necessary to understand the actual individual in order to get value from individual-level data. The ability to differentiate between individuals and their behaviour on a website, without any intention of having an on-going conversation with those individuals, can also be tremendously valuable.
An example of how to use individual-level data is to assess the impact of specific campaigns marketing success through knowing the 'quality' of visitors driven by each campaign, based on their website behaviour and the propensity to convert. This insight enables organisations to optimise their campaign spend by each channel in order to maximise return on their marketing investment.
Progressive organisations are going even further by applying predictive analytics to this data early in the campaign cycle, enabling them to build visitor value models that predict which campaigns will drive website visitors with the highest chance of conversion. Based upon this insight, marketing teams can adjust their advertising spend early in order to focus their budgets on those campaign executions most likely to result in revenue.
- Saving costs through understanding channel preferences
It is critical that organisations understand which channel their customers are interacting with in order to maximise sales opportunities. An example of this is catalogue companies that are experiencing a considerable shift of revenue from call centres to online sales. Despite this shift, many still continue to invest heavily in paper catalogues and mailings due to a fear in reducing sales if they cut back. Through understanding which customers actively use catalogues in their purchase decisions and which do not, organisations can make intelligent and data-driven decisions about where investment can be cut without risking an impact on sales.
Organisations armed with data such as whether the customer searches websites using offline catalogue codes, or whether they use the direct mail or the email promotional code on the check-out page, enables brands to differentiate between those customers that actively use catalogues and those that don't. As well as reducing catalogue costs, this insight can also be used to tailor marketing and advertising messages.
It is clear that considerable business benefits can be gained from bringing together the distinct, but complementary, disciplines of web analytics and database marketing into the new era of digital database marketing. By marrying these two disciplines, techniques, data types and mind-sets together, organisations can generate even more value from their analytics and marketing teams. This will truly help to increase customer loyalty, improve new customer acquisition and enhance the customer experience for long-term growth and success.