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Three challenges you must face head on to build an analytically-driven organisation

Technology & Data

Three challenges you must face head on to build an analytically-driven organisation


Craig Rutkowske and David Kelly talk us through how a truly analytical marketing business must understand its customers, and develop actionable insights.

Marketers for mid-sized organisations today face a complex set of issues. The number of channels continues to grow, customer expectations keep climbing and internal demands to drive revenue and reduce resourcing only adds to the complexities.

These factors force you to get the most out of every dollar. And the best way to do that is to become an analytically driven marketing organisation, which requires mastering three key areas:

  • Understanding the customer: The most important aspects are customer behaviour and customer value. Understanding how a customer engages and the value they create for the company enables marketers to determine where to dedicate resources.
  • Developing actionable insights: Determine the products a customer may be interested in or actions a customer may take enables marketers to tailor messages to customers’ interests.
  • Putting those insights to work: Making insights part of daily marketing is the sign of a truly analytical organisation – using personalised messaging for each customer enhances their experience and delivers value to your business.


If you don’t know your customers, it is impossible to develop actionable insights. The best way to know your customers is to explore the data you collect about them. Any data is useful, and it should all be considered.

Once your customer data is in a manageable format, then data visualisation can help you actually see what the data is telling you.

Data visualisation empowers marketers. It can help you look for hotspots or unique customer segments. For example, visualising sales data might reveal that certain regions have low penetration and you can design campaigns to improve sales in those areas.

Marketers often struggle to get current campaign performance data. By the time it arrives, the campaign is often over. Data visualisation can enable real-time dashboards so you can quickly adapt ongoing campaigns while ensuring the next campaigns take advantage of lessons learned.

As digital marketing channels continue to expand, the importance of understanding how a customer engages across channels rises too. Data visualisation enables marketers to understand which paths on a website are working and how customers convert (and how they don’t).

Having a rich, detailed understanding of your customer is a foundational step in becoming analytically driven. This foundational knowledge can be used to develop actionable insights.

Insights: strategic versus customer-level

Insights are information that can make decisions more focused and precise. It can be developed at the strategic and customer levels.

Strategic insights are high level and enhance the marketing plan by providing a roadmap for achieving marketing goals for specific time periods.

Customer insights provide a deep understanding of strategic customer segments and how they are differentiated across the customer life cycle for important marketing metrics (value, growth potential, loyalty and attrition risk). Customer insights also enable personalisation of each customer’s experience. Strategic and customer insights are needed to help marketers and companies to achieve their corporate goals and customer marketing objectives.

For example, a strategic insight might reveal that a product is forecasted to perform poorly in coming months. As a result, the marketing team may decide to increase spending on search engine marketing to direct traffic to the product page on your website. This type of insight is often developed using special tools tuned for forecasting so that an organisation can adjust their plans to create the best outcomes.

But customer-level insight lets marketers know how to engage with specific customer segments. Should a mid-market retailer send out a 25%-off coupon to every customer in their database, or only those that won’t make a purchase without it?

The answer is obvious, but many retailers end up doing the former. Predictive models, developed with data mining software, can help marketers understand which customers will to make a purchase without a coupon and which won’t.

Developing insight enables marketers to make an impact, but only if they deploy them. This means putting them to use in marketing efforts.


Putting insights to work

Strategic insights enhance inform the customer marketing plan and strategy by providing focus and establishing priorities and facilitate marketing mix decisions.

Marketing attribution is a great example of a strategic insight; it helps you understand which campaign touchpoints actually account for conversions. Marketing attribution helps you make budget allocation decisions that may affect every campaign.

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Marketing attribution timeline

Predictive models can be embedded into the customer experience using campaign management software to automatically select and deliver the best offer. Campaigns using analytically selected offers results in increased revenue decreased cost per interaction.

Real-time decision management software can use the same models to enable marketers responsible for interactive channels to implement real-time analytically driven decisions where context and timing are critical.

For example, whether or not to attempt to retain a customer trying to cancel their account or what level of marketing investment to make to retain a customer based on their current and future value.

Development of these sorts of insights is the foundation of becoming analytically driven, but using those insights in every interaction is the sign of a truly analytical marketing organisation.


Craig Rutkowske is a customer intelligence expert and David Kelly is a senior solutions consultant at SAS.


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