Data is central to the success of CRM initiatives. Knowing who your customer is, how they behave and what they want makes a CRM strategy successful. Data gathering can begin even before your prospect becomes a customer. Matching a prospect’s profile to the product or offer is the first step.
But data on its own is meaningless if it is not analysed and acted upon. Through analysis, data can be turned into insights, which can then inform the various CRM processes and, indeed, the business itself.
Data should underpin the way each touchpoint is utilised to build loyalty.
Consider the consumer who shops on her store card at a retail outlet. Her transactions are recorded against her card and she is sent offers that detail the latest fashion trends and earns points on her card shopping for these. At some point, her transactional data shows that she has started shopping for baby clothes so she can now be cross-sold products to do with babies, and rewarded with double points when she buys them. Now she is increasing her spend in the store, cross-shopping for both herself and her family and being rewarded for this, thus ensuring that the retail outlet is offering her value and retaining her business.
A good CRM programme begins with data. Who are my customers and what do they want? What are their demographic and psychographic needs? Why did they choose me in the first place? How many of them are active, and continue doing business with me? Why do the others stop? What is the average tenure of a customer?
Read more about this in the Data analytics chapter
Often, you will need to research this information. If the company has a database, conducting surveys, focus groups or dipstick telephonic research can help you get an idea. Consider that an Audi Q7 driver is vastly different to an Audi A1 driver, for instance. They both pick the brand for similar reasons, but their motivations behind choosing the products differ vastly.
Data can give you these insights. It can enable a company to create real value for the customer and thereby gain true loyalty. There is little point in running a customer insights survey, looking at the results and saying, “that’s interesting” without putting into action any changes suggested by the results. Not auctioning noticeable changes also means customers are less likely to take part in surveys going forward, and quite rightly so, what’s in it for them? Conversely, if you do action changes, customers will feel increased ownership in the brand and its offering.
Choosing a CRM system and operationalising it in your business is no small undertaking. Cost can be dramatically impacted by how well this system can integrate with other processes and tools in your business. Make sure to do your homework before jumping in.
The actual technology you use to gather and collate data is also crucial. Remember that there are many facets to CRM, and the quality and accessibility of the data will have a major impact on how well these processes run.
When looking at data, it is essential to keep in mind the Pareto principle. The Pareto principle, or 80/20 rule, holds that in many situations approximately 80% of profits are delivered by 20% of customers. Also keep in mind the traditional view that 20% of customers are responsible for 80% of problems related to service and supply (Koch, 2008).
This means designing solutions with efforts directed at the 20% of customers who generate the most profits. To do this, you should segment customers effectively. High value segments are unique to each business.
You’ll also want to consider the exact data to collect. While this will depend largely on your business objectives. Here are some considerations.
- Information should be commercially relevant.
- Capture additional contact details from the customer at every organic interaction such as on purchases, contracts, negotiations, quotes, conversations.
- Allow your customers to manage their data along with you.
- Capture any information you send out to the customer.
- Consider anything that adds value to the relationship.
- Note any legal implications around capturing and storing data, particularly web-based behavioural data, as the user’s privacy must always be taken into account.
Where and how to gather CRM data
CRM data is gathered from a variety of touchpoints. Let’s look at some of the possible opportunities for CRM data capture and analysis. Each avenue discussed below collects a range of data from whichever touchpoints the business deems valuable.
Traditional CRM system data
Most traditional CRM systems are used to capture data for sales, support and marketing purposes. On top of simply creating a central repository for data access, these systems and their related databases also offer basic analytics. The actual range of data collected within the traditional CRM system is dictated by the CRM objectives. For instance, data could include:
- Demographic details on potential leads, current leads and contacts, such as contact information, age, gender, and income
- Quotes, sales, purchase orders, and invoices (transactional data)
- Psychographic data on contacts such as customer values, attitudes, and interests
- Service and support records
- Customer reviews or satisfaction surveys
- Web registration data
- Shipping and fulfilment dates, such as when orders were shipped and delivered.
Data mining and testing hypotheses
Data mining involves analysing data to discover unknown patterns or connections. It is usually conducted on large datasets and looks for patterns that are not obvious. Data is analysed with statistical algorithms that look for correlations. It is used by businesses to better understand customers and their behaviour, and then to use this data to make more informed business decisions. For instance, women might traditionally be shopping for nappies during the week. On the weekend, men may become the primary nappy-shoppers. The things that they choose to purchase on the weekend, such as beer or chips, might dictate different store layout over a weekend.
Data mining is typically performed by computers, which can sift through massive amounts of data and find tiny but significant patterns that a human researcher may overlook.
Analytics data is generally captured through specialised analytics software packages. These packages can be used to measure most, if not all, digital marketing campaigns. Web analytics should always look at the various campaigns being run. For example, generating high traffic volumes by employing CRM marketing tactics like email marketing, can prove to be a pointless and costly exercise if the visitors that you drive to the site are leaving without achieving one or more of your website’s goals.
Read more about this in the Data analytics chapter
Social media monitoring data
There are many social media metrics that are important to monitor, measure and analyse, and some of these can provide valuable insights for CRM implementation. This can cover everything from quantitative data about number of fans and interactions, to qualitative data about the sentiment towards your brand in the social space. Social media metrics can also lead you to new prospects.
Collating and organising your data
Typically, you’ll find that a business has:
- One or more databases such as email, customer, mobile, or call centre databases, or datasets in silos.
- A point of sale system where product purchase data is stored.
- Various forms of web data from display or search networks, keyword research, site analytics, social media, or email marketing.
- Social media profiles on sites like Twitter, Facebook, or LinkedIn, which can also be considered databases of sorts.
CRM software can be used to automate lead and sales processes, and to collect all of this customer information in a centralised place, allowing a company to get a holistic view of the customer; from this, meaningful data insights can emerge.
Have you ever had a frustrating service experience with a brand? How did you feel about the brand afterwards? Large organisations need a single view of the customer to avoid frustrating them.
Organisations can be large, and a customer often speaks to several members of the organisation, depending on the nature of the communication. It would be extremely frustrating for the customer to have to explain all previous dealings with the organisation each time, and equally frustrating for an organisation not to know who has spoken previously with a customer and what was dealt with. This could be a touchpoint at which a company falls down, and leaves a less than positive impression with the customer.
Fortunately, there are many technological options that help to record all this information in one place. Most of these services can also schedule elements of the sales process, and set reminders where appropriate for follow-up action.
Consider user adoption rates and the cost of time and integration when making a decision about which CRM software to use.
Some notable examples include Salesforce (www.salesforce.com), Genius (www.genius.com) and Highrise (www.highrisehq.com) from 37signals. Bespoke technology tailored to business problems can have remarkable results.
Keeping data fresh
Call it what you will, but ‘stale’, ‘outdated’ or ‘unhealthy’ data doesn’t benefit anyone. Some generic older data can help you assess trends over time, but identifiable customer data is usually redundant if not up to date. People move house, update their contact numbers and email addresses, and change jobs. They earn more or less, stop working, start working, have kids, and retire. All of these mean that their needs change, and their contactability changes, so maintaining a customer relationship and delivering relevant communication becomes impossible if your information is not fresh.
So, how do you keep your data fresh?
For generic data (like web analytics), you must continuously monitor trends and note what causes changes over time. This is also useful for monitoring trends and identifying gaps in data when a business evolves. For instance, if you know that you generally receive increased website and store visits during December, but your sales drop, you know that you need to gather more data around your inventory and in-store environment during that time.
Keeping identifiable data current means you need to facilitate regular dialogue with contacts on your database. Whether it’s through a call centre, an online prompt or a quick question at your in-store point of sale, there needs to be a plan for updating details at regular intervals. You can empower your customers and incentivise them with programme attractiveness.
Analysing data for marketing
One of the most powerful features of interactions and transactions over the Internet is that everything is tracked and recorded (see the Data analytics and Conversion optimisation chapters). This provides a wealth of data that can be analysed to make business decisions.
For CRM, this means that the customer acquisition source can be recorded and analysed against sales data. This leads to a very accurate return on investment (ROI) calculation and indicates where CRM and marketing efforts should be focused.
ROI stands for return on investment – and it’s key to understanding whether marketing efforts have been successful. Here’s a simple example: Company A sells accounting software and makes R10 000 on each product it sells. It sends an email to its customer base, users who have bought a previous version of the software and might be interested in upgrading. The campaign has an overall cost of R100 000. Of the 5 000 users who receive the email, 10% decide to buy. That means it cost R200 to acquire each of the 500 customers. The company has made R5 million, an ROI of 50:1.
The key to effective use of technology in CRM is integration. Ensure that all channels can be tracked, and that information is usable to all parties within an organisation. Knowing where your customers come from, but not what they purchase, is pointless: these two metrics need to be compared in order to produce actionable insights. Analysing CRM data can aid marketing initiatives in a variety of ways.
- Campaign analysis: Find out which marketing campaigns are leading to the best returns so you can refine them and increase ROI
- Personalisation: Customise your communications to each customer
- Event monitoring: Tie offline events, like shows or sales, to your online interactions and sales
- Predictive modelling: Predict a customer’s future behaviour and meet this need at the right time.
Mobile marketing can play a key role in offline events, after all, the mobile phone is portable and connected to the internet, meaning that users can engage a brand directly on location.
Improved customer segmentation, including:
- Customer lifetime value (CLV) analysis: Predicting each customer’s lifetime value and managing each segment appropriately, for example, offering special deals and discounts.
- Advanced customer profiles that identify certain behaviours, such as big spenders or those who look for bargains by attending sales. This information can be used to tailor marketing communications accordingly.
- Customer prioritisation: Target small groups of customers with customised products and service offerings that are aligned to meet customer needs, rather than simply generic current offerings. You should craft specialised retention strategies for customers with the highest CLV.
- Identifying brand influencers and advocates. Consider the realm of social media, where influencers are central to the spread of content. Brands are increasingly prioritising relationship building with social media influencers to build brand advocates who will help market the business for them. By identifying which customers are providing the most value and positively influencing others to become customers, you can focus efforts towards them and increase their loyalty, creating true brand advocates.
Understanding customer lifetime value
CLV is the profitability of a customer over their entire relationship with the business. Businesses need to look at long-term customer satisfaction and relationship management, rather than short-term campaigns and quick wins. This approach leads to increased value over the entire lifetime of a customer and means that CLV is a metric central to any CRM initiative.
It’s important to look at your customer base and segment them according to how often they purchase and how much they spend with your company. Very often, customers who spend more cost more to acquire, but they might also stay with the company for longer. Referrals made by a customer can also be included as part of the revenue generated by the customer.
The key is to understand these costs and then target your CRM strategies appropriately. CLV lets you decide what a particular type of customer is really worth to your business, and then lets you decide how much you are willing to spend to win or retain them.
For example, a potential customer looking to purchase a digital camera is likely to search on Google for cameras. As a company selling digital cameras, your excellent search advert and compelling offer attract the potential customer, who clicks through to your website. Impressed with your product offering, the user purchases a camera from you, and signs up to your email newsletter as part of the payment process.
Analysing the amount spent on your search campaign against the sales attributed to the campaign will give the cost per acquisition of each sale. In this case, this is the cost of acquiring the new customer.
As the user is now signed up to your newsletter, each month you send her compelling information about products she may be interested in. These newsletters could be focused on her obvious interest in photography, and highlight additional products she can use with her new camera. Think about the value exchange that is necessary for a customer to give you their attention. Content marketing is a powerful tool here. The costs associated with sending these emails are the costs of maintaining the relationship with the customer. When she purchases from you again, these costs can be measured against the repeat sales likely to be made over the course of the customer’s lifetime.
See the chapter on Content marketing strategy for more on the importance of offering customer s value.
Assuming that a customer buys a new camera every three years, moves up from a basic model to a more expensive model, perhaps buys a video recorder at a certain point. All of these allow a company to calculate a lifetime value and ensure that their spending on a particular customer is justified.