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Data is an incredibly valuable business commodity. Yet in such a digital-first world, the volume, velocity and variety of this data can be complex to process. Because of this, a number of sophisticated data analysis tools came to market, requiring expert skills to deliver meaningful insights.

Today, however, data is integral to strategic business decisions; it has moved out of the IT room and into the boardroom. More business users want real-time access to these insights to see a full 360° view of their audience, customers, and prospects. This is where self-service analytics prove instrumental.

What is meant by the term ‘self-service’ analytics?

Gone are the days when companies would need a specially qualified technician to make sense of
their data. Self-service analytics make use of intuitive dashboards to give staff at every level of
the company access to key data. This data can include:

Business capability data

Business capabilities are core elements that structure a company. In data terms, this is the information and insight a business needs to execute its business strategy. This may consist of how we store customer data, track their purchasing behaviour, as well as what we can do to improve our service and increase conversion rates.

Stakeholder/beneficiary data

In order to understand and get buy in for your strategic direction, you need insight on your stakeholders and their needs, not just your own. This may include how the business complies with legislation and best practices, new products, marketing activities and sales, and frontline staff performance.

Target data

To build a robust sales strategy, you need to understand your existing customers’ behaviour to understand future customers’ needs. This data may include customer records, product databases, customer service and sales history.

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How can we aggregate this data?

Self-service analytics is a form of business intelligence (BI) in which business users are enabled and encouraged to perform their own queries and produce business insights with nominal IT support.

 

This is beneficial because it does not require any third-party tools: everything is available to you, either in our own company records or from sources such as macroeconomic data. Self-service analytics help us to approach all data analysis from a customer-centric point of view. Therefore, we can look to the following sources for data:

 

Internal
Internal data is information that is gathered and processed from different branches within your organisation. Examples could be: purchase orders from the sales team, transactions from accounting, reorders from inventory management, leads from marketing, email engagement, and any other internal sources that collect information about your customers.

 

External
This is data that is not collected by your organisation. This data is obtained from any source outside such as social media channels, demographics, location information, website behaviour, macroeconomic data, customer check-ins and public data.

What do we do with the data once we have it?

All this data can give rise to what’s known as a data lake: a vast volume of information which needs to be contextualised to give it any value. Let’s use an example: we start with the core transactional data such as a customer record, before branching out into enterprise data such as sales history, and finally into broader data such as search engines.

Consolidating this data creates a Single Customer View (SCV) which provides businesses with the ability to track customers and their communications across every channel.

By following this flow, we could, for example, find a specific customer, view his/her buying history and then look up the product as a search term to assess the competitiveness of the keyword. This in turn would affect our marketing strategy, perhaps focusing on ranking for this organic keyword to increase sales. This is just one example of using a ‘lake’ of data, starting at the core single customer and extrapolating it to make a business decision.

Self-service analytics in practice

This 360° analytics model can be used across a number of different industries. Let’s see some examples from our Customer360° range.

Travel The travel industry relies on many different types of data including individual travellers—previous flights, transport data such as flight times and travel data such as the destination.

By compiling all of this into a data lake, we can make high-level business decisions – for example, predicting when to offer deals based upon how early customers book.

Media Digital value is notoriously difficult to quantify, but by using a 360° analytics model, we can assess performance from a cross channel experience, campaign, channel and consumer data management perspective.

Like travel, we can use predictive analytics based on historical behaviour. For example, if we create segments based on previous user groups’ buying habits, we can predict that they may be interested in a product at a particular time, and direct our marketing activity towards them during that period.

The benefits of self-service analytics

Did you know that 2.5 quintillion bytes of data are created each day, and most companies have at least 100 terabytes of stored data? That’s an incredible volume of data to be collected, let alone analysed. This complex mix of structured, semi-structured and unstructured data sources makes up the data lake, from which decision makers can get their own full 360 degree understanding of their customers.

Self-service analytics empowers business users to query this data directly, rather than relying on third parties. This information gives additional power to marketing campaigns, identifies the most profitable customers, and saves monetary and financial resources from otherwise poor decision-making.

Self-service analytics gives enterprises more autonomy, starting small and broadening outwards to get the most value out of their data. For more information on implementing self-service analytics, and getting the best from your customers with our 360° suite, contact Genisys today.
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