Is Power BI the answer to data overload?

So is Microsoft Power BI the answer to data overload? Microsoft Power BI is a tool that lets users build interactive dashboards and visualisations. It sits in the Business Intelligence category and competes against Tableau (part of Salesforce) as well as some others.

With any tool that promises the world, it’s good to understand why you might choose to use it (over other tools), what the (hidden) costs are and how to get the best out of it.

What’s to like about it?

The benefits –

Microsoft power BI features and benefits
Microsoft power BI features and benefits

The Short comings –

Microsoft power BI features and disadvantages
Microsoft power BI features and disadvantages

Intelligence, but not understanding

As you can see above, a tool like Power BI can give you everything at your fingertips. But it still doesn’t address a fundamental problem: data is complex, especially from multiple sources, so you need to decide what you want from it and the most effective way to structure it. We’ve worked with many, many clients that have the software but not the expertise in designing data models that deliver not only intelligence, but understanding.

The solution?

Dealing with multiple data sources and messy data is our bread and butter, it’s what we do every day and we simplify as much as possible throughout the process.

What you need really depends on the sophistication and/or availability of resource in your company. If you have a team of data analysts/engineers/scientists then they will no doubt have the expertise to build the solution from scratch. We’ve seen some great examples using Snowflake as this layer.

How mature is your data analysis capability?

How mature are is your data analysis capability?
How mature are is your data analysis capability?

If the team of analysts isn’t there or they’re just too tied up with BAU, then an automated central data repository can be the solution. More and more we’ve seen a Customer Data Platform becoming the answer.

UniFida is the fully featured customer data platform for insights driven marketers. Hosted in the cloud, it ingests and unifies data from all online and offline customer behaviour, including web browsing, ecommerce transactions, customer order systems, email service providers, SMS, direct mail, call centres and even retail. It then uses personal identifiers to build a single customer view.

So is Microsoft Power BI the answer to data overload? Microsoft Power BI is fully integrated into UniFida, giving marketers faster access to meaningful insights.

For a chat, a demo, help supporting a business case or all three get in touch today.

How does the UniFida customer data platform work?

How does a customer data platform work?

Ingesting and integrating data

So how exactly does a customer data platform work? The first element in understanding how a customer data platform works is ingesting data. CDP’s ingest customer data from multiple sources. Typically, these will include website data, paid digital, transactional, direct mail, retail, email, and call centre. All data received by a CDP will relate in some shape or form to a customer. The data is usually sent to a CDP using an API or via an SFTP site.

Customers have multiple identifiers and these change over time, such as mobile phone number, email address, cookie ID, postal address, customer reference or landline number. This data is collected, and these identifiers are used to generate a single customer view also known as ‘Identify Resolution’. For example: if someone logs into your website with their current email, but with a different cookie ID, then the new cookie ID is added to that particular customer record on the assumption that they are using a new device. Equally, if a new transaction record is received with the same customer reference, but a new address, then a new address is added on the basis they have either moved or added an extra residence.

As new data is ingested, each record goes through what is called the ‘purning’ process. This is the stage at which the record’s personal identifier(s) are matched against all other customer records that are held in the CDP until a match is or is not found. At this point the data may be matched into an existing single customer view or a new one created. Each recognised customer is given a permanent unique record number or ‘purn’.

Identity resolution

Is at the heart of a CDP and is central to all the rest of its functionality. A good CDPs’ functionality is rooted in the knowledge that people have multiple identifiers, and that these identifiers can all change. Over time many or all of these identifiers are likely to change for an individual. The CDP should keep a history for every one of every version of these, although regarding the latest versions as most likely to be current. This collection of identifiers is what it calls on to build the single customer view.

The data in a CDP is held in what is called a schema. This is the way in which the data is organised. Every organisation using a CDP will need their own schema although within an industry, schemas will have a lot of similarities.

Engineering derived data

Engineered data is important for the value it provides for selecting specific customer groups for communications or developing customer insight. It can comprise any variable that can be calculated using an algorithm or other means from the raw data in the customer data platform.

Data engineering can take many forms, from simple examples like banding variables such as age, to more complex ones like keeping a counter on customer’s total historic value. A major use of engineered data is in developing and recording scores derived from algorithms such as propensity models.

An example of an engineered data field is where we want to know what each customer has contributed to a business after the cost of acquiring them. We can then:

  • Use historic purchase data for each individual in say their first and second year since recruitment
  • Deduct the cost of acquisition which can be derived the channel they came in from
  • Deduct the cost of communications sent to them in the same period which is held in the contact history area
  • Calculate an individual customer contribution

Engineered data is updated at an individual level every time a relevant event happens; so, each new home shopping purchase, eCommerce transaction or physical retail transaction can lead to a changed score in the engineered data section. A great benefit of engineered data is that it allows you to base axis for charts or selections for campaigns on these additional variables.

Analysing customer data

A CDP is essential for gaining a full and accurate understanding of customer behaviour. For instance, without a CDP that combines web browsing history with transactions, it would not be possible to understand the relationship between the two. Again, if individual contact history is not held against a customer record then the effectiveness of campaigns that are sent to the customer, and to which the customer may respond through different channels, cannot be accurately measured.

The CDP builds the single customer view, and it is against this that customer analysis can take place. It provides the dataset that becomes the one authoritative source of information about customer behaviour for an organisation. With this in place decision makers have a firm basis on which to proceed.

There are so many aspects to the analytical tools that can be used to analyse customer data that there is little merit in trying to list them all. Some are built into the CDP and others require data to be first extracted from the CDP and then transferred to them. What matters is that they have the best possible customer data set to analyse.

So, the results from customer analysis form the basis on which key decisions about customer marketing can be made. These include such areas as:

  • Customer acquisition (targeting and channel choice)
  • Digital planning
  • Product development
  • Customer relationship management
  • Salesforce management
  • Pricing

Even corporate mergers and company valuations.

Given how important these decisions are, it makes good sense when designing a CDP to first start with a list of the kind of results that will be required from customer analysis so that for instance data is held with sufficient granularity to make these possible.

Connectivity to external systems

The CDP can support other systems in their personalisation and management of customer communications. Typical examples are:

  • Providing customer selections for email marketing systems
  • Customer segmentations for web personalisation technology
  • Names and addresses for postal marketing
  • Target audiences for social media

So just as the CDP ingests data from multiple sources it also provides selected data to external systems. These connections are usually made via an API or via transfer of data to an SFTP site.

Delivering personalised customer experiences

Within the CDP we expect to find functionality for the selection of specific customer groups either on a one-off or on a recurring basis. These groups are usually selected for output to external systems that manage the actual communications. The selections themselves can be simple based on Boolean logic rules, or they may be more complex based on propensity scores applied within the engineered data. They can also be based on triggers, such as a new customer having just been recruited.

The CDP needs to enable these different types of selection, and crucially record what contacts each individual customer has been selected for. Functionality is also required for test and control, and for including source codes with the selection.

Associated with delivering personalised customer experiences needs to be functionality for measuring the results of campaigns. This is often automated within the design of the CDP and should always include the ability to attribute results such as orders back to campaigns, even if they respond through different channels.

What are the costs for a customer data platform?

What is a customer data platform?

So what exactly is a customer data platform?

As customers we generate massive volumes of data as we engage across multiple channels using different devices which makes it challenging to capture, integrate and activate this data effectively.

Data repositories are often siloed and not integrated with each other or allow easy transfer of data to marketing platforms. Let us now throw in some GDPR and updates from Apple with identifier for advertisers (IDFA) and the deprecation of third-party cookies by Google.

Today’s customers simply assume that your company knows and remembers who they are, what they have done, and what they want, always and across all channels. Their expectations are high, and tolerance is low. So it is not surprising to see that many marketers have made a unified customer experience their highest priority.

What’s the problem with data?

Not having a single customer view creates many challenges including:

  • Making it more tedious to activate campaigns to the right audience and report on them in a timely manner.
  • Degrades customer experiences.
  • Introduces privacy concerns.

Marketers and marketing technologists know that gathering and acting on unified customer information is not easy. In fact, only a small percentage of companies have achieved this and can truly operationalise their first party data. The rest are battling with technology, strategy, budgets, organisations, staff skills, and other obstacles to success.

Traditional methods for collecting that data into unified customer profiles, such as an enterprise data warehouse, have failed to solve the problem. Newer approaches, like “data lakes”, have collected the data but failed to organise it effectively and enable marketers to activate the data into owned and paid marketing channels.

The Customer Data Platform is an alternative approach that has had great success at pioneering companies. The process of collecting and unifying the data is known as identity resolution which is a core building block for enabling better customer experiences and optimised marketing effort. A CDP puts your marketing team in control of the data unification project, helping to ensure it is focused directly on marketing requirements.

CDPs apply specialised technologies and pre-built processes that are tailored precisely to meet marketing data needs. This allows a faster, more efficient solution than general purpose technologies that try to solve many problems at once.

Customer Data Platform Definition

“A Customer Data Platform is packaged software that creates a persistent, unified customer database that is accessible to other systems”.

This definition has three critical elements:

1. “packaged software”: the CDP is a prebuilt system that is configured to meet the needs of each client. Some technical resources will be required to set up and maintain the CDP, but it does not require the level of technical skill of a typical data warehouse project. This reduces the time, cost, and risk and gives business users more control over the system, even though they may still need some technical assistance.

2. “creates a persistent, unified customer database”: the CDP creates a comprehensive view of each customer by:

  • Capturing data from multiple systems.
  • Linking information related to the same customer.
  • Storing the information to track behaviour over time.

The CDP contains personal identifiers used to target marketing messages and track individual-level marketing results.

3. “accessible to other systems”: data stored in the CDP is then made available to other marketing systems for analysis and to manage customer interactions.

What should a customer data platform do?

In essence, a customer data platform combines all your customer data from online/offline sources and unifies this into a single customer view to enable cross-channel activation and personalisation.

A CDP should integrate into existing and future marketing/advertising technology enabling you to decide which channels to communicate with your customers.

It should enable automated reporting of activity on your key marketing metrics. And of course, it should support GDPR enabling you to check customer consent, action subject access requests and the right to be forgotten.

How does a customer data platform work?

In praise of use cases…

(or ‘anvandningsfall’ as they were originally termed)

Back in 1987 a Swede called Ivar Jacobson presented the first known article on use cases as a means for capturing and specifying requirements for computer systems. He didn’t much like their original long Swedish name and eventually settled on ‘use case’ which has since been universally adopted.

So why are we singing their praises?

Use cases do not require technical knowledge, they allow your teams to collaborate on the desired business outcomes and uncover gaps. One of the key things with a use case is it ensures your stakeholders have defined the business need and, how the activity will be measured.

An example of a marketing use case is “Use data to deliver relevant, personalised omni-channel campaigns in order to increase revenue and reduce marketing costs”. The use case is pretty straight forward. The brand wants to communicate with their customers across multiple channels in order to generate revenue and potentially reduce wasted marketing spend.

Many businesses fail to develop core use cases to solve a problem or deliver on a strategy. By developing core use cases, which are prioritised based on the business goals and can be measured, it will give you the north star to focus on and deliver against your goals.

We see at least five stages in the process of successfully introducing marketing technology where they are of crucial importance.

First by going through the discipline of articulating and documenting use cases a business can clarify exactly what they want this nebulous item, a marketing system, to actually do.

It provides a non-techy way for the requirements to be mapped out so that the user community can articulate step by step what both it and the system are expected to do, and what the outputs should look like.

It also allows for consideration of time. When and how quickly should processing happen including volume. Thus, allowing the system providers to get a handle on whether for instance they are dealing with ten thousand or a million customers.

Given that nowadays almost all martech is purchased off-the-shelf rather than being built inhouse, the combined use cases can help start the process of vendor selection. Rather than being told a long list of the glossy features that can be delivered by the martech salesperson, most of which you don’t want in the first place, the company can factually check whether the system being proposed can actually do what you require.

Next, the use cases can feed directly into developing the business case. If for instance you are going to be able to do A that you couldn’t do before, how much customer value are you going to be able to generate compared to where you are now. Or alternatively how much staff time will be saved using the new tool to deliver B more quickly?

We find that business cases for martech generally span across four key areas:
1 The incremental revenue generated by being able to do something that was not possible before.
2 The cost of time saved by using a better tool to deliver something more quickly.
3 Reduction in technical debt by streamlining and unifying data and platforms.
4 Reducing reputational risk by having clear GDPR measures in place

Once past the business casing stage, many organisations will want to start with a live proof of concept or POC. If you select a few areas where the new technology should add value, and where it can be set up and configured quickly (please note I am not writing LHF!) then a POC can be put in place.

There is no better way to finally confirm that everything works from the technology to the customers responding to it. In addition, a live POC that works, gets quick buy in from all levels in an organisation. The POC will also pick up on what is not working and enable you to put it right.

And finally, when the full martech needs to be set up and configured, the developers can take the use cases as the specification against which they are going to have to deliver. The company can sign off the configuration as done when the use cases work.

At UniFida we like to help our clients with developing their use cases at the start of the process of introducing a customer data platform. We do this for all the reasons articulated above, and incidentally it helps us understand quickly whether we can in reality deliver what you need.  Having developed several client use cases, we can help stimulate your thinking around what they might provide.

Our offer! We have made a decision not to charge for this kind of consultancy as it helps you understand what you need the technology to do, and for us to understand what we may be called on to deliver.

Please do get in touch if help with developing use cases for a customer data platform is what you are looking for.

UniFida logo

UniFida is the trading name of Marketing Planning Services Ltd, a London based technology and data science company set up in 2014. Our overall aim is to help organisations build more customer value at less marketing cost.

Our technology focus has been to develop UniFida. Our data science business comes both from existing users of UniFida, and from clients looking to us to solve their more complex data related marketing questions.

Marketing is changing at an explosive speed, and our ambition is to help our clients stay empowered and ahead in this challenging environment.