mindtalks brand growth: The Four Pillars of an Effective Data Marketing Strategy – ExchangeWire – picked by mindtalks

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In this article written exclusively for ExchangeWire, Martin Smith, Director of Strategy and Innovation at Jaywing, discusses four key elements which marketers need to consider in the activation of consumer data.

When it comes to marketing, data is everywhere. There has never been such a wealth of customer data available from so many different sources. And while it can be tempting to capture as much information as possible, without the right strategies to properly understand and effectively use this data, you won’t get far. If data isn’t structured for analysis or if no marketing data strategy is in place, brands often struggle to make meaningful use of it.

Data on its own does not lead to actionable insight. It requires analysis to become information, and context to become insight. From the insight, a compelling story can lead to strategic change. This requires the right tools – and the right people. But when data is understood and optimised, it can be used for many things, including:

  • Understanding customers’ values, desires and needs
  • Enabling marketers to provide personalised experiences, offers and messages to customers
  • Predicting customer behaviour
  • Understanding customer purchasing journeys, product associations and purchasing barriers

 
To help understand how to use data to power your business, the following four key pillars underpin an effective data marketing strategy.

1. Collect

Martin Smith

Martin Smith, Director of Strategy and Innovation at Jaywing

The first step in any data journey is capturing the information in the first place. You should only capture what’s needed at each stage of the process, but you can unobtrusively obtain vast amounts of insight from consumers in the behaviours they exhibit, both on and offline. From email addresses to complex understanding of customer behaviour patterns, consumer data can be collected from every single interaction with your customers; all while ensuring that the data captured and how it is used is in line with relevant regulations.

Different forms of data confer different advantages. Information and context gained from speaking directly to customers offers a depth of detail that is difficult to get elsewhere, while online touch-points can provide an overwhelming wealth of valuable data. Insights gathered from online behaviours are determined not only by what the customer purchased, but also which products the customer searched for, viewed, and considered.

2. Analyse

After this information has been collected and collated, the next step is understanding what can be learned from it. In short, this phase is about connecting the dots between the data and the wider objectives to enable you to work out next steps.

Analysis is not the same as reporting. Reporting tells you a story about the numbers. Analysis involves asking questions, forming hypotheses, testing, measuring and learning.

Analysis can lead in all kinds of directions. For example, learning what additional products are a natural fit for a customer whose search patterns see them exploring a particular product, style or model, or working out what’s causing drop-out without purchase journeys.

3. Model

The analysis stage will often lead to a need to model the data to further explore the levers that drive sales or lead to greater customer engagement.

Two types of model are prevalent in marketing – propensity models and attribution models:

Propensity models use your past data to predict what customers might do in the future. For example, how likely is this customer to respond to an email campaign, or make a repeat purchase? Use these models to drive personalisation and targeting.

Attribution models use all marketing touchpoint data to reveal the contribution that each online and offline activity contributes to the overall sales outcome. This allows accurate and effective allocation of marketing budgets, ultimately driving incremental growth through understanding the impact that different customer interactions have across the buying journey.

The analyse and model stages are all about working out what’s right for your business, prioritising marketing investments and addressing strategic problems, whether that’s retention, loyalty, acquisition, brand growth or even determining pricing. It’s not a one-off process; rather it’s a regular journey of testing and learning as your customers’ characteristics, attitudes, and motivations, evolve, enabling you to attract, nurture, and grow, your customer base.

From the modelling stage, the information can then be turned into clear and actionable insights for implementation in your marketing strategy and activity.

4. Action and optimise

Analysis and modelling provide a great platform from which action can be taken, focusing on marketing optimisation.

Attribution models should be used to change the mix of marketing activity, dialling channels up and down to get the best return on investment, and making sure that nothing is wasted on unremunerative campaigns, removing customers from activities that aren’t of interest to them.

Meanwhile, one of the most effective ways to optimise marketing activity is to use models and insight to provide personalised customer experiences. In a world of increasing automation, personalised interactions with customers are becoming more profitable. But this can only happen when customer data is utilised intuitively and effectively, and when it is structured to deliver actionable insight.

At this level, personalisation that is far more than providing offers and deals relevant to the individual’s interests can be achieved. Brands should strive to provide relevant, contextual communications across all channels, whether that’s via website, through direct mail or email content, and delivering to the consumer a joined-up experience that’s tailored to them and their interests. It is data, coupled with the right tools, that can be used to drive all of this.

Of crucial importance is to look beyond simply the headline stats in the data to discover customers’ real needs, motivations, and preferences, allowing perfectly timed and targeted communications that positively influence action to be delivered.

In today’s market, utilising customer data is no longer an option to consider. It’s a must. Customers are looking for a frictionless experience, alongside the human touch. A tall order, perhaps, but it is achievable. Understanding your customers, how they behave and exactly what they need through smart use of data is key to accomplishing this.

Source: exchangewire.com

 

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