Just 1.3% of respondents to a 2018 Winterberry and IAB survey said they were “extremely confident” that their organisations have the right expertise, experience, and skills to get the most value out of their data. More than 87% responded that of the skills most needed to maximise data usage, data analytics was at the top.

A worrying trend for what should be a golden era of data-driven marketing that’s delivering not only performance for brands but also exceptional customer experiences.

So what are the data strategies required to make digital marketing campaigns a success?

Data and personalisation

Most marketers know that personalisation is the most powerful way for ads to stand out, especially in the noisy digital world of today. Brands that have been unable to successfully incorporate data into marketing quickly discovered that their efforts were practically invisible to an expectant, demanding and often fickle customer-base. Simply put, if an online experience isn’t up to scratch, it’s ignored.
All of this means that data needs to sit at the heart of everything a brand does.
When shoppers browse for products, watch videos, or generally surf the web, they leave a trail of digital data. These patterns and behaviours can reveal valuable insights which can be used to predict their needs and inform your marketing strategies.
But many brands aren’t using data correctly or know which kind of data is most relevant. As brands react to this new reality, the delivery of personalised online experiences has quickly become a non-negotiable.  

Data and omnichannel

It’s a retail reality that shoppers today are beginning their path to purchase on one device and ending it on a different one, or starting online and completing the sale in-store and vice versa.
But the fact of the matter is that many retailers simply don’t have the data volumes to deliver truly shopper-centric strategies. They simply don’t know enough about their customers. Many are still struggling to recognise individual shoppers across fractious channel behaviours. 
Armed with information on shopping history, social media habits, and even geo-location, retailers can personalise experiences better than ever before. But the data must be processed and interpreted intelligently to deliver real-world results, both online and offline. 
Get this right, and all of this information can be used to generate content that actually matters to the consumer when it matters most. The businesses that are able to achieve this complete view of their shopper will survive and thrive in this new world.

Data and collaboration

But achieving these results isn’t easy. Brands, retailers and agencies alike, so often at loggerheads when it comes to what information belongs to who and how it can be shared, recognise the data opportunity and are collecting and analysing it as quickly as they possibly can. 
This could mean retailers sharing real-time POS and inventory data with brands, giving both companies access to system insights to better plan for promotions and operational efficiency.
However, as challenging market conditions persist these three interested parties are finding that new ways of working built around deeper collaboration and sharing are delivering huge returns and helping all parties escape the confines of working solely with walled gardens (Google, Amazon etc.) and marketplaces. 
There is a conscious realisation among these three types of business that their collective data sets are stronger than the sum of their parts. This approach is enabling them all to respond to rapidly shifting consumer behaviour through cooperation. 
In isolation, each in this trio has significant data gaps, leaving them with an incomplete picture of their customers and delivering a stunted experience as a result. But the conversation between the three has become more common in recent times as the parties involved look to overcome challenges around data sovereignty and gaps in technology with a view to forming a holistic view of the customer on which truly impactful campaigns can be built. 
The tasks of linking these disparate sets of data, understanding the picture they paint and accessing actionable insights are by no means easy. That’s why this party of three is gradually evolving into a quartet, to incorporate technology partners that can facilitate not only the free movement of data between all involved but enable a faster and more innovative analysis of what that information says about their customers. 

Data and measurement

But the power of data doesn’t end at delivery. The use of information is essential to evaluating the success of modern marketing campaigns. Many brands have discovered that traditional evaluation metrics including ROI have been short-term in their scope and reinforcing of negative trends. 
Today, a thorough analysis of data in the evaluation phase of a campaign can ensure marketers are taking a more strategic approach to campaign measurement. Customer Lifetime Value (CLV) is the total value a consumer brings to a company throughout their lifetime. In its simplest form, CLV is calculated by adding up the revenue earned from a customer over their lifetime and then subtracting the initial cost of acquiring them.
While it may seem like a no-brainer to measure campaigns in this way, our 2019 research highlighted some significant challenges in getting full value from data. Key data barriers to the increasing use of CLV identified include tracking customers cross-device (30%), inability to collect data due to users not being signed in (23%), and an inability to track single-use products (21%). Gaps in these areas can have a high impact on the accuracy of predictive CLV calculations. But CLV is a huge opportunity for businesses to enhance the quality of the service it provides to customers. Making the best use of available data can facilitate this.

Data and AI

But it’s impossible for marketers alone to manually personalise ads at scale, assess the volumes of data available to them or implement their insights. 
Machine learning and artificial intelligence (AI), is helping to ensure experiences are customised for every shopper, at a volume and speed no human can beat. 
As a result, according to IDC, the retail industry recently overtook banking to become the industry leader in terms of AI spending, investing $3.4 billion on use cases such as automated customer service agents, expert shopping advisors and product recommendations, and merchandising for omnichannel operations. 
Using AI, retailers can create models to help understand customers’ desires, motivations and actions across both physical and digital channels. These models support improvements in a range of functions including more targeted and personalised marketing campaigns and enhanced promotional efforts. Decision-makers are well aware of this potential; a recent IBM study of more than 1,900 retail and consumer product leaders forecasts that the adoption of intelligent automation in the retail space will rise from 40% of companies today to more than 80% in three years.
Fast-changing consumer preferences and an ever more competitive market environment require retailers to be lean and adopt a predictive approach to retail. By identifying and learning from patterns in large volumes of data, AI can help companies quickly adapt to an increasingly dynamic market environment.
AI can also help retailers to make better strategic decisions on the future of their business. This includes optimising existing store space and locations and predicting future store performance when expanding their physical footprints.
Performance marketing today depends on data. It’s fuelling more impactful, more personalised campaigns, enabling businesses to measure their successes more accurately and helping them deliver scale that was previously impossible. The brands that are successful in their data strategies will be the ones who win out in the long run and successfully navigate the tough times we’re seeing across the marketing world right now.