December 4, 2018 Blog Articles

Turning Big Data into Revenue, in Real Life

By Gil Sperling, CTO at Popimedia

This opinion piece was featured in the Social Media Landscape 2019. Click here to gain access to the full report.

As South African consumers become more connected and engage with brands across even more touchpoints, businesses find themselves collecting cyber-tons of data in evermore locations.

While Big Data is by no means a new concept, turning these facts and figures into money-making marketing is still an elusive skill, one we have seen some leading brands benefit from hugely through the use of advertising technology this year. And the expectation is that more and more brands will be augmenting their online efforts with offline data, as the bottom-line benefits are undeniable.

Firstly, there is a need to collate and integrate fragmented client behaviour data across business units; from eCommerce stores, websites and digital advertising platforms to call centres, CRMs and point of sale. And while this process may be challenging or lengthy for some organisations, the resulting marketing capabilities and business results are invaluable.

The advantages to harnessing this big data are then two-fold for marketers;

  1. The hard-hitting impact of campaigns can be measured to the nth degree, right down to business profitability per new customer, and in real time.
  2. Live custom audiences can be fed back into digital campaigns, optimising the success of messaging and ensuring scalable client journeys.

While working with brands to collate and optimise their holistic data, we have witnessed industry-leading results; for lead-centric sectors, like financial services (FinServ), automotive and education, as well as for more basket-based industries, including retail and travel.

 

Closing the Loop Between Marketing Metrics and the Bottom Line

All too often, marketing functions struggle to attribute sales figures and profits to a certain campaign or digital marketing initiative due to fragmented sets of data within an organisation. Generally, the number of leads generated and the cost per lead are reported on, but the quality of these leads is measured post-campaign, by manually comparing sets of data. This is not ideal, as highly accurate, real-time measurement is required to make proactive decisions and maximise the efficiency of campaigns.

Through the use of automated ad-tech, live sales data can now be used to measure the success of campaigns, right down to an ad within an ad-set.

How does it work? A unique identifier is attached to each lead that is generated through an ad, which then progresses through all the online and offline platforms, collecting data along the way, until it is converted into a sale, the data of which is fed back into the advertising platform to reflect the real-time business value of each advert.

Essentially, it is the implementation of a tracking pixel in real life.

The knowledge is powerful, as it provides clear indicators of ROI right down to the very image which is most profitable for the business. But it also provides an opportunity for marketers to optimise their digital campaigns for the results which actually matter to business (beyond engagement indicators like clicks).

We have seen great success within the financial services sector with brands that now optimise their campaigns for profitability per new customer over time. The retail industry has also gained eye-opening insights through these big data solutions; now being able to attribute off-the-shelf sales of specific products at identifiable outlets right back to a single social advert. As a result of a dramatic increase in leads, drop in cost per lead and higher conversion rates, we have been able to rapidly scale our clients with these solutions.

 

Using Data to Optimise Marketing Efforts, in Real Time

Measuring meaningful results in real time is just one part of it. Remarketing to custom live audiences is a powerful resultant of big data that brands often neglect.

Clients (potential and existing) effectively become part of a different audience every few days as their journey with a brand progresses and their needs change. A face-to-face conversation with a spanking new client would be vastly different to a drink with a regular brand evangelist. Similarly, content in digital adverts should change to suit the evolving needs of clients. After all, the relevancy of content correlates directly with its engagement and success.

This becomes imperative for repeat services like insurance, who derive profitability from high retention rates. Their customers need some love to decrease buyer’s remorse, but how does a brand talk to them authentically throughout their buying cycle and still be able to scale this interaction?

Tools like our audiencing information system enable companies to utilise their aggregated big data to dynamically move clients between pre-defined audiences (based on events), and thus serve them with the digital adverts that fit their current mind-set. It is smart remarketing, done accurately and at scale.

The beauty of this capability is that it supports the marketing strategy for any business directive; from retention to upsell and cross-sell.

 

What it Takes to Get There

In truth, it can take many months to get this right, and even then there is continuous improvement as more data sources are identified and integrated. Technical departments need to be dealt with, along with risk-management divisions and all the other red-tape within a corporation. Not to mention the business’ culture and speed, along with the distribution and structure of data (for example; CRMs, point of sale, loyalty data, inventory and any franchisee-specific data).

But in the same breath, simple versions of data integration can be implemented within weeks or days, and the results become apparent within weeks. Partnering with an ad-tech company that adheres to all data privacy requirements also aids the management of risk. If an open-minded approach is taken, and the willingness to be iterative (and not go for the Rolls-Royce solution upfront), companies could be turning their big data into revenue in no time at all.

Our clients always wish they had integrated their data into marketing campaigns much sooner.

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