As we have seen in chapter 1, there is a lot of data available that comes from different resources and can be of many kinds. But what is the most relevant data for us start-uppers? If you answered “Customers” you are totally right, since without customers our companies cannot be successful. In this module we will see and understand the different types of customer data analytics and the various insights they can provide.

Understanding Customer Data Analytics

Remember what is analytics? It is basically the collection, reporting, and analysis of data. Customer Data Analytics does not differ much as it is the process of collecting and analysing customer data, but in this case to learn our customers’ behaviour and preferences that will help us making efficient business decisions.

These analytics can be of two kinds, quantitative and qualitative. Can you think of the 5Vs of Big Data? The Variety plays a key role here!

• Quantitative analytics involves looking at the hard data, the actual numbers, meaning that is statistical and is typically structured in nature. This data will alert you to problems as for example it will inform you of an increase of customer churn.
• On the other hand, qualitative analytics concerns subjective characteristics and opinions, it will give the why to the problems found on the quantitative analysis. Qualitative data is non-statistical and is normally unstructured or semi-structured in nature. Coming back to the examples, the qualitative analysis can inform us that customer churn was caused by customers being confused about how to use the product.

Types of Customer Data

Start-ups can use diverse customer-related data to run customer data analytics, which today are mainly sourced of Big Data rather than traditional data. In this module we will look at the 5 main customer data types to find out how we start-uppers in different industries can use them.

Source: https://www.freepik.es/vector-gratis/personas-gestion-relaciones-comerciales-pie-alrededor-datos-documentos-estadistica-servidor-portatil-crm-texto_11249912.htm#page=1&query=crm&position=22

Advertising (Ads) data

Let’s start with the first type of Customer Data. Are you familiar with ads? If not, you have probably seen them in your Google search results, they are normally listed in the first positions. Ads data uses automation and machines to mechanize and streamline the delivery of information to our customers. In other words, it takes into account all of the data you have about customer behaviour and applies it for more meaningful interactions.

Web traffic data

Website traffic refers to web users who visit our company website. Web traffic is measured in visits, or “session” as you will later see, and is commonly used to measure a company website’ effectiveness at attracting an audience.

e-commerce data

E-commerce data refers to all the information we can get from all areas that have an impact on our online store, if we have one. We can use this information to understand the trends and changes in customers’ behaviour in order to make data-driven decisions that will help to increase online sales. For example, a client might visit many webpages to have a satisfactory purchase that provides a high quality branded product at the best price. All these movements made by this client will create a large and valued amount of data that we can make use of.

CRM Data

CRM data is the volume of data a company can get from its customers and users of its website or Social Media through their purchases, navigation and data left in their different actions. This might sound similar to e-commerce data, but also comprises information regarding likes or shares amongst others. By analizing this data, we can establish a more effective strategy for attracting customers to visit our platforms.

Net Promoter Score data

Last but not least, and connected with CRM Data, we find the Net Promoter Score (NPS) Data.  This data reveals how many customers are willing to recommend a product or service to other people. This is one of the most important KPIs a Start-up should monitor as it gives a direct insight into customer satisfaction and brand loyalty.

The Data Cycle

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Now, we know what Customer Analytics are and the types of Customer Data you can get. But where do you start? The best strategy is to use the data cycle. The data cycle is a popular way to help you make the most of the information collected from your customers in your online platforms. You can use it to help you prepare, action, and inform your business decisions online. The four main stages of a data cycle are: Plan, Do, Check and Act.

Let’s explore the data cycle in action. Imagine a marketing team of a Car Manufacturer company wants to organise a digital advertising campaign to encourage people to buy electric cars instead of combustion engine cars.

Plan

In the “Plan” stage, the team would identify their goal for this campaign and outline how they plan to promote it. They decide their goal is to see a 18% increase of electric cars sales over the next two months using email and social media marketing.

Do

Next up is the “Do” stage of the cycle. This is when the team writes the content of the email, and designs the Social Media strategy and launches the campaign.

Check

One month after the campaign has ended, the team measures how many people opened the emails and assess whether the campaign had an impact on the number of customers buying electric cars. They notice that while the social media campaign had a good engagement due to the promotional videos, very few clicked or opened the emails sent. This insight highlights that the email technique should be redesigned. This is the “Check” stage of the data cycle.

Act

Finally, the “Act” stage reveals where a business can use their findings to improve future campaigns. In this case, the marketing team could decide to redesign the email strategy by reviewing the content they’re sending or change it for another action such as Ads/paid search to help improve the engagement.

Insights of Data

Now we understand that we can collect Big data from our customers and apply analytics but how do I interpret this data analytics? Here is where the insights come. Insight is the value obtained through the use of analytics, in other words, collecting data is important, but knowing what to do with this information is what can truly add value to our Start-up.

Let’s see a quick example:

Imagine that you have an e-commerce website:

• Data might show that your users had 5000 sessions in the past month.
• Analytics could show you how many sessions occurred on Chrome browsers in Norway.
• Insights could reveal that that sessions on Chrome browsers in Norway were 45% less likely to buy.

Regular and Actionable insights

To put it simply, a regular insight is analysing ‘why’ something has happened. Regular insights are critical to determine actions and help you focus on what is important to your start-up goals.

On the other hand, an actionable insight takes this analysis one step further and determines what to do next, so that you can successfully improve and refine what you’re doing.

To uncover your own actionable insights, try following these six steps:

1. Define your goal: Outline what do you want to achieve with your campaign.
2. Collect the data. Gather and organise any statistics or information relevant to your goal.
3. Interpret the data: Analyse trends and spot patterns to see how this has affected meeting your goals 
4. Develop recommendations: Provide justified suggestions on how to improve business practices based on what you have learned from your data analysis
5. Take action: Put your recommendations into practice and create an action plan to test your suppositions. 
6. Review your outcomes: Evaluate whether your actions have had the desired impact and make note of how you can further optimise to improve results.

Let’s look at an example that explores how data can lead to actionable insights.

Martha is the Community Manager of a Nautical Environmental NGO. They need new members to join the organization and she has set a goal of getting 500 new members registered in the next 6 months. As her goal is to increase membership, the data she collects from her analytics software include how many people completed the sign up form, which online channel they used to register, and how many people shared posts on social media.

Analytics reveal that social media channels are the main source of sign-ups, so the next step for Martha is to find out which social media posts were most effective at driving registrations.

When analysing and interpreting data, she finds out that already registered members sharing the NGO social media posts on their personal accounts generated the highest number of new registrations.

From these regular insights, Martha deduces that registered members encourage more sign-ups by sharing the NGO Social Media Posts.

In order to turn this into an actionable insight, Martha now needs bring an action to that data research that will make a real effect in the NGO. For example, she could design a series of social media posts that provide easy instructions on how to become a member of the NGO so registered users can promote the NGO across their own social network.

Now that you have learnt how to produce actionable insights from data, think about how you can use your online data to help make the best decision for your start-up.