The data-driven future of retail

marketingHow online data is enabling brands to

understand customers at an individual level

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really know their customersDo retailers

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Digital data is now essential to anticipating and influencing customer journeys. Without it, retailers are missing key information to enable personalisation and understand where marketing spend is most effective.

Retail CMOs have a lot to contend with. From intense competition and increasingly price conscious consumers, to tight budgets and continual pressure on margins, how can brands strengthen their proposition while pursuing cost efficiencies?

Understanding customer behaviour is the key to delivering a better service and increasing revenue. But the path to purchase has never been more complex.

With customers hopping between multiple channels and devices, many marketers are struggling to keep up. Just 8% of retailers use full cross-channel personalisation*.

Online data is often underused and isolated from in-store purchase history, loyalty cards and offline CRM—which individually only provide a glimpse of the whole story.

The result is a disconnect between the online and offline journeys. And the resulting gap in customer insights prevents retailers from serving the hyper-personalised experience that customers have come to expect.

Retailers can now connect the dots between a customer’s online and offline activity via multiple channels and devices, and combine them with offline CRM and purchasing data to create a comprehensive Single Customer View (SCV).

This enables retailers to close the knowledge gap and answer key questions like:

• How can we attract more high-value customers?

• How can I increase the value and loyalty of a customer?

• How can I increase retention?

• When does the customer journey break down?

With advanced data capture and predictive modelling, brands can generate powerful insights and deliver timely, tailored communications, increase conversion rates and nurture a loyal customer base with more efficient, cost-effective promotions.

In this whitepaper, we reveal how individual-level customer data can influence every point on the path to purchase. We also share best practice advice to accelerate your journey to unlocking the power of individual level data.

What is individual level customer data?

Individual customer data is the merging and linking of all data gathered on a person, including all online and offline engagements. This can encompass a customer’s buying history, the marketing they have been exposed to, browsing history, device usage, search history, as well as any personal information they have shared (e.g. name, age, mobile number, email address) and demographic data declared.

* Statistics in this guide are sourced from Jaywing’s 2017 data-driven research report

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Digital data complements long-term marketing strategies with agile communications, driven by customer behaviour.

By capturing the full path to purchase for all consumers, retailers can better understand customer journeys to deliver more personal and effective communications throughout each stage of the customer lifecycle, from acquisition to advocacy.

Making itpersonalpersonalpersonalpersonalpersonalpersonal

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Effective acquisition

Target new customers with the most effective communications

Individual customer data drives the customer acquisition strategy by identifying which marketing activities successfully nudge a new customer along the customer journey from awareness, through consideration to intent to purchase.

It is also possible to overlay customer segmentation information (e.g. demographics or life-stage) and behavioural data, such as actions completed (e.g. searching specific keywords on the website, using store locators or adding to favourites).

These can be included in models to understand which customers are more likely to convert. Retailers can then target these – and similar – customers, with awareness driving display ads.

Armed with this insight, you could use tools like Google Customer Match, Facebook Custom Segments and DoubleClick to focus ad spend on those specific individuals, resulting in more efficient targeting.

Digital data presents a significant opportunity to understand which marketing activities effectively convert new customers. And just as importantly, where you may be wasting budget on needlessly driving awareness for frequent customers.

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Focus your marketing spend on where it can add most value—attracting new, high value customers

Similarly, it is possible to link PPC terms that successfully converted new customers, to the Life-Term Value (LTV) of those individuals. This allows retailers to determine which keyword phrases are more likely to attract higher value customers. For instance, generic versus brand terms. This helps to justify bidding on higher value keywords (or niche terms competitors have overlooked).

Conversely, many retailers waste a lot of PPC spend on frequent customers. For example, after analysing traffic for just a month, a retailer could estimate how much it was spending per year on loyal customers, i.e. those who had purchase history and had returned to the site multiple times.

By analysing which recurring customers were clicking on ads, then assessing the risk of excluding them, retailers could then reinvest the spend on acquisition—and predict upfront how many sales could be generated from the new, accurately targeted advertising.

Track and influence extended consideration phases

The ability to influence the consideration phase is invaluable for retailers with more expensive products, like cars and furniture.

It’s not uncommon for consumers to take longer than six months to decide on the purchase of a sofa, for instance. Also, for such items, customers typically fail to convert on their first visit to a physical store—leaving a lot of revenue to play for.

A furniture retailer could use digital data to identify the proportion of customers who browse the website before going in-store, and track all brand engagements before and after each visit.

Analysing social media activity could provide likely candidates for influencer marketing and display ads. This enables the retailer to build up a picture of what is influencing each customer’s decision.

They can then target them with the most appropriate communication to close the sale.

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Turn single sessions into recurring transactions and increasing repertoire.

By linking in-store data with online data, retailers can identify new and returning customers and provide incentives to become repeat buyers. It’s no easy task, but new technologies and methodologies are beginning to make it more achievable.

Pre-empting customer needs Cross-purchase modelling enables brands to identify products from different categories that are most likely to appeal to each customer.

So, for instance, a furniture retailer could combine past purchases, browsing history and social engagement to determine which customers fit the profile of new homeowners—and entice them back at the appropriate time with a bed for the spare room.

Rewarding loyal customers Similarly, loyal customers could be rewarded through personalised incentives. So an organic food supplier might send tailored messages based on previous purchases, customer behaviour, propensity and margin.

A customer with a high calculated long-term value and a high propensity for cross sell—having browsed the chocolate category but not purchased—might get this message:

“If you like our speciality coffee, perhaps you’d like to sample our Madagascan chocolate? Buy now and save 20%.”

A similar customer with a lower calculated long-term value could be offered a lower discount rate.

Reduce basket abandonment

Individual-level data can allow brands to understand and reduce the triggers that lead to basket abandonment. For example, it can show if abandonment occurs on the page where delivery costs were first displayed.

You can then hypothesise that this indicates that the delivery costs are considered disproportionate to the item cost. In this case, you can introduce an incentive to overcome this barrier for potentially high value customers.

The data can also allow marketers to identify situations such as:

• When a basket is being used as a shopping list—where a consumer adds multiple similar items while they decide which one they want.

• Who is likely to checkout organically within a given time period—and who could be persuaded with targeted ads.

• Where the basket was abandoned—enabling businesses to classify individuals who abandon on the checkout page as higher value prospects than those who abandon earlier.

Data inspired

devotionData can uncover insights into a customer’s behaviour, including search terms, browsing history, and dwell times, and predict their next actions in real-time - enabling retailers to deliver personalised, contextual communications to enhance customer value and loyalty.

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Keeping their

attentionAdopting proactive retention strategies to lock in satisfied customers.

Understanding individual-level data, rather than broad segments, allows retailers to engage customers with highly specific content and achieve a degree of personal interaction that boosts retention and establishes brand loyalty.

Take a pet shop. It’s one thing knowing that a person owns a dog or a rabbit, but understanding those pets more can enable the business to enhance the individual’s experience.

Knowing things like the lifespan and lifestage of those pets, what the individual likes to look at on site, their purchase journey and the triggers that may indicate those journeys are changing or speeding up — such as someone browsing dog collars when they usually buy rabbit food.

This allows you to tailor communications to maximise your customers’ experience over time.

Stop lapsing and basket abandonment before it happens.

Traditional lapsing programmes use blanket rules for all customers, targeting anyone who hasn’t shopped in 30 days, for example. They typically assess sales, but ignore the subtler cues that precede an imminent lapse. Crucially, conventional lapsing programs are reactionary. But why wait till your customers leave before doing anything?

Predictive lapser programmes can measure brand disengagement, such as decreasing dwell times and page views, alongside missed sales and days since last visit. They can then deliver proactive communications to individuals at risk of lapsing.

Has a disengaged fashion customer seen their favourite designer’s new range? Perhaps they’re tired of the same style and looking for a new look?

Retailers can use individual level data to develop bespoke lapsing models to tackle disengagement. For example, you can use predictive modelling to pre-empt customers lapsing. This allows you to communicate to individual customers with relevant marketing or incentives to re-engage before they were lost completely.

This same technique can be used for basket abandonment by automatically offering the next best product to price sensitive customers, for instance. Anticipating such hesitations and offering alternatives can secure revenue and drive loyalty.

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From buyers

to brand advocatesAsking for permission is the first step to nurturing personal promoters.

Brand advocacy is driven by customer-first thinking. The cumulation of seamless and highly personalised communication programmes helps retailers elevate regular customers into committed advocates.

The new General Data Protection Regulation (GDPR) puts power in the hands of the customer, ensuring brands contact them in the right way or not at all.

However, customers that give you permission to use their data in line with GDPR are more likely to engage with you. GDPR compliant data that is current, accurate and structured can also help you to understand your customers and build compelling, personalised offers for them.

By leveraging data, retailers can maximise budget efficiency and reward customers with an enhanced experience and bespoke loyalty promotions to retain their business—and gain their consent to contact them.

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Revealing the true impact of your

marketingAlthough this paper focuses on the use of individual level data at a micro level—to enhance the relevancy of a customer’s personal marketing experience—it is worth noting that this same data can be aggregated and used to maximise the macro marketing investment strategy.

Data-driven attribution modelling, built up from individual level-data can allow marketers to see where to focus marketing spend.

Individual-level data enables brands to attribute marketing activity to sales across multiple channels and devices. This means marketers can discover which activities and interactions successfully contribute to a customer converting.

Our recent research found most marketers (69%) agree that accurately attributing value across channels is vitally important to their organisation.

Yet the majority (82%) say they are using a single-touch point method, such as first-click and last-click attribution, to evaluate the success of marketing. And only 3% strongly disagree with their organisations approach.

Sophisticated attribution means retailers can track engagement across the entire conversion path and ensures that all touchpoints are duly credited, from first to last and everything in between.

By adopting this approach, all channels are valued accurately, creating a clearer view of where marketing is having its greatest impact.

See how Swinton are combining online and offline data to fuel advanced attribution.

Micro data informs macro thinking and underpins better investment decisions - across multiple channels and devices.

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Delivering advanced attribution for Swinton Group

By leveraging Jaywing’s digital tracking technology, Almanac, and its unique individual-based approach to attribution modelling, Swinton Group is able to focus its spend better to achieve improved levels of ROI.

By adopting this approach, Swinton Group will be able to follow an individual’s journey through online and offline channels across the branch network, and attribute the right value to each interaction for each consumer, as opposed to only attributing at channel-level.

“We appointed Jaywing because it has the right technology and data, modelling and marketing expertise to undertake attribution at an individual customer level, and to evaluate the performance of our online and offline marketing.”

Stephen Cox, Head of Analytics and Pricing, Swinton Group

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Insights from the expertsAchieving a truly personalised marketing experience throughout the customer journey requires the following:

1. Knowing what you want to achieve, then collecting relevant, comprehensive data

2. Selecting a solution that is capable of capturing, managing and storing your data

3. Analysis to reveal impartial insights that drive action

4. Compliance with the General Data Protection Regulation (GDPR)

In this section, we offer guidance to enable you to maximise the potential of individual level data.

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Identifying your challenge

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An initial reaction could be to collect everything and then just dig through the information to see what it might reveal. But this is a hugely inefficient approach.

Instead, consider your business problem carefully. What question are you trying to answer? Invest time in clarifying the challenge you are trying to solve and ensure you gain business and stakeholder alignment.

It is often beneficial to have expert support to tease out these ‘exam questions’.

Being clear and strategic in your data capture will focus your efforts on solving your challenges.

Knowing what problem you are trying to solve or question you are trying to answer will drive the decision regarding what is the “right data”?

However, for all required data sets, it is important that you collect a comprehensive and complete picture.

For example, if you want to understand how your marketing channels interact and build on a customer’s path to purchase, it is important that you include data on all interactions both on and offline, across devices, and capture onsite browsing.

Ask yourself, before you ask the data

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Finding your solution

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The retail industry is complex, but that doesn’t mean your data capture and storage solution should be black box and impenetrable. Make sure that the data is raw and transparent and that you can access it, so that it can be easily manipulated and insights found.

Data management is highly specialised. Choose a partner with a sophisticated methodology and a proven track record. This will take the pain out of this critical phase and reduce the time of implementation.

Integrating the data storage platform with a fulfilment and reporting solution, such as email service providers, display networks and MI tools, is critical. Some businesses prefer the convenience of an all-in-one platform, but the ideal is to integrate best-in-class solutions.

Choose a partner that is capable, willing and experienced working across multiple technologies. This will give you the flexibility to make technological changes as your business needs change.

When you require external specialist support, you need to maintain ownership of your data and assets. Partners can support the collection, management and storage of the data for their tenure, but ownership should be with the business, ensuring flexibility to select the right partner at the right time.

No matter which system or systems you choose, it all comes back to quality of data. One high quality dataset can generate endless insights when fed into different applications.

Choose carefully

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Analysis and

action

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Data can reveal powerful insights if you let it. It can also be impenetrable or misleading.

Remember that when interpreting information, everyone can be influenced by cognitive biases—sub-conscious factors that influence the way we think and the decisions we make. Factor in commercial pressures and ‘group think’ and you risk subconsciously interpreting data to support your initial hypothesis (or justify sunk marketing costs).

That’s why it’s important to adopt a rigorous analytical model.

Firstly, as already outlined, know where your data has come from, ensuring that it is in its raw form, and be confident of the data management processes. Then interpret it by generating hypotheses and trying to disprove them, ensuring your tests reach statistical significance.

The initial phase of analytics is often revelatory, with answers challenging pre-defined assumptions, this leads to enhanced understanding but further questions.

It’s important to test and learn, seek uplifts, and exploit new found opportunities. And remember that tests with unexpected outcomes often provide the most valuable insights.

It is a constant balancing act between a rigorous, methodical approach and being commercially pragmatic. Between providing in-depth insight and a customer-centric, actionable view, while adapting to external factors that influence purchases—from predictable seasonal uplifts to unexpected events.

Half of retailers (50%) say a lack of skills and capability is their main obstacle to improving data management.

If – like 50% of marketers – you have a skills and capability gap in your organisation, you can look to partner with external specialists who have experience solving these challenges and who can support you.

People are fallible. Ensure your analysis is impartial

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GDPR compliance

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Data is core to personalisation, but permission is critical. Retailers must ensure they collect and process customer data transparently and respectfully, in accordance with the GDPR.

Long-term customer relationships are built on trust and mutual benefit. When customers share their data, they should be rewarded with an enhanced experience and fair value exchange.

Users should be clearly informed how their data is being used and given the ability to opt in or out and request for their data to be forgotten.

Retailers must also be aware that they shouldn’t retain data for any longer that is necessary and data shouldn’t be shared without customer permission.

Stay compliant with the General Data Protection Regulation

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There is no silver bullet that can serve the levels of individual personalisation and customer experience that marketers aspire to. This can only be delivered through a blend of the right technologies, the right data, the right people and the right skills.

Right technology, right data

The biggest challenge for retailers is the lack of an off-the-shelf solution that can deliver the level of insight and personalisation required. Certain solutions offer some relevant functionality, but consistently have two limitations:

1. It is challenging to harness individual digital data in a manner that can be matched or merged with customer records and offline sources

2. Digital customers’ devices are only remembered for a limited period (e.g. the last 120 days), after which they look like a new user.

We found that delivering the most personalised customer experience desired by our clients required raw digital data at an individual-level.

Collecting and owning this raw data over an unlimited period allows ultimate flexibility and enables matching with customer data and multiple on and off-line sources to generate bespoke insights and personalisation strategies.

To achieve this, we created our own easy to implement, digital data tracking and management solution called Almanac that overcomes. these challenges and meets our clients’ needs.

Almanac serves the data in a smart format that can be analysed and interrogated, revealing insights and delivering individually-tailored customer experiences as described in this paper.

Right people, right skills

But technology and data alone will not solve your business problems. How the technology is implemented and the way the data is harvested, interpreted and used is critical to success.

The best approach is to enhance internal teams with specialist partners who deliver the right skills and flexible team support as required—from marketing strategy development to deployment.

The optimum team combines creativity, data science and pragmatism, all with a focus on finding marketing solutions that are both customer-centric and commercially viable.

There is no silver bullet.

Conclusion

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About Jaywing

We also like our project teams to be flexible, so we can meet the demands of your project with the right resource at the right time. At the helm is always a lead consultant, adding to and shaping a project.

We deliver initial results in weeks, not months, by taking one step at a time, on a prioritised path, and delivering value at each step on the way to your ultimate goals.

We pride ourselves on being easy to talk to and engage with. We don’t have a fixed or inflexible approach to managing and analysing data, rather a series of methods to choose from, according to the circumstances.

Where we have been frustrated by technology in the past, we have developed our own solutions to overcome the problems. Such as our Big Data platform and our digital tracking software, Almanac.

Put simply, our aim is to help our clients find new ways to approach business and make changes for the good. Above all, we are pragmatic and realise the importance of hitting the ground running, delivering value early in the engagement.

To find out more about individual online data, or Almanac, our digital data tracking tool, get in touch!

almanac.@jaywing.com

Our team of over 70 specialist consultants have spent their careers on both sides of the fence, facing the same types of issues you face and are expert in our key speciality areas. They know what it means to go from idea to implementation. They relish the challenge of seeing a project through from the beginning to the very end.

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More best practice guides from

Jaywing

For your own copies of these guides, please email hello@jaywing.com

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0333 370 6500 hello@jaywing.com jaywing.com

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