Quick guide to better CRM data. How to turn your CRM into a clean and effective tool that adds value to your sales pipeline

Transcription

1 Quick guide to better How to turn your CRM into a clean and effective tool that adds value to your sales pipeline

2 1 Sales teams are measured on the numbers of new accounts made. Marketing teams are measured on the quantity of new data acquired. Customer service teams are measured on how cost effectively they deal with requests and complaints. No one is measured on data quality. issues that we come across Marketing finds it frustrating that they are not always able to accurately track which events have led to the most qualified leads. Lots of client details on the CRM are incomplete so there is a significant waste of time and effort going back to the original event data to try to correct entries. Marketing have lost faith in the CRM and for important events they create separate spreadsheets to circumvent the system. Data entry is often completed by the sales teams. Sales teams want to get potential customer details into the system as quickly as possible to get them into their sales funnel and do not always accurately capture where the lead came from this frustrates the marketing team who need this information to measure the success of their work. How can you integrate diverse data requirements whilst giving each department incentives to co-operate with data entry that is meaningful across the organisation? It is often difficult to perceive the work pressures of colleagues from different areas of the business. Therefore it is a good idea to start the audit work within one business team to highlight bottlenecks that the team can resolve itself. Customer service feedback can be in unstructured notes fields and yet that data could provide extremely valuable product or service feedback to the sales team.

3 2 No silver bullet to data quality It does take work to keep data sources clean, current and useful. Data quality is a concern for most companies. The good news is that once you know where the dirty data is entering your systems you can start to plan the clean-up. It is always difficult to tackle a problem that at first appears overwhelming. Data cleansing does not have to be done in one big bang approach. We advise clients to tackle data issues in an incremental way at first and learn from each project and take that learning on to each new stage of the cleansing process. Here is our 6 step plan to improve STEP 1: Bad data is perfectly normal Identify your customer s data journey. Document the key data elements of your customer journey. Map your current customer data to check that entries are correct and at which point on the journey those details were added to the customer record. Play detective and find out where useful data is missing and then create a KPI (key performance indicator) to keep your teams aligned in addressing that particular issue. The KPI will also help you to identify often simple improvements in capture. Bad data isn t like dirty washing - to us it is perfectly normal and anyway we enjoy washing it! Don t be embarrassed by it and let us help you to address the issues and make it better.

4 3 STEP 2: Hidden cost of bad data The longer it takes you to process your data the more likely it is to impact on the customer journey resulting in delays in response times. Data cleaning tools can highlight incomplete data prior to data being entered on to the CRM at source. This can save time and money further down the data journey. Create data quality targets and regularly run reports to score the quality of your data. This will start to highlight any patterns that are developing. By committing long term to CRM data quality you will develop more sophisticated targets and better training for your data entry teams. Now we make sure that each time an order is made it automatically triggers a sales follow up call on the order system. STEP 3: Don t do everything at once Start with key areas of your and focus on making small improvements within one team. The team is more likely to invest energy in making the improvements and implementing the KPIs if they can see a direct benefit to themselves. Each team member can write a list of top 10 data issues and this will help the team manager to prioritise which issues to tackle in what order. Often it helps to work backwards from a customer complaint to help identify what should be included in your top 10.

5 4 STEP 4: Finding problems is a good thing By carrying out a data profiling report on a particular data set in one team you can see what you are dealing with. Once you have identified the major issues you can start to develop some rules to solve those data entry issues and to assign KPIs to monitor the effectiveness of those solutions. It is important for this process to be done in non-blame culture. STEP 5: Is the data relevant needs to be accurate and up to date otherwise your business teams will lose faith in its relevancy and start to use workarounds. Try not to rely on multiple spreadsheets alongside your CRM and risk data being kept in isolated silos with poor sharing of customer intelligence. There is no point spending time and money collecting data if you do not then go on to use it in a meaningful way. Invest in a data storage architecture that will allow you to access the data easily and integrate with reporting and analytics systems. STEP 6: Now you can start to predict Faith in data quality within your CRM means you can generate reports to consider the ROI of your marketing activity. Use analytics to look for developing patterns and then integrate your own data with external sources, such as weather forecasting or socio-economic indicators, to inform your planning. Once we looked carefully at our data we were able to understand where our most valuable clients were coming from and it changed the way we invest in our campaigns.

6 5 Case study A medium sized events company based in the South East of England providing event packages to business clients. The company uses a variety of different types of marketing campaigns to drive engagement and to generate addresses for their prospect CRM. More and more frequently it is the sales teams who are responsible for the data entry into the CRM, they tend to focus on the entry fields that are most relevant to sales and do not prioritise the fields designed into the systems by other departments, such as Marketing. The company know that their customer journey from prospect through to sales, then onto customer service, is a bit clunky and there is duplication within the systems so one client can have multiple entries within each system. Customer service is handled over the phone but increasing volumes of contact are being dealt with via social media using Twitter and Facebook. The company has grown steadily but is now finding the increase in sales has seen a disproportional rise in bad data quality. There is limited information flow between customer service and the sales team; the executive team suspect this is starting to have an impact on referrals and repeat sales. No one team takes responsibility for maintaining data quality and managing the processing of the data journey through the different systems throughout our organisation.

7 6 Implementing an action plan Data quality analysis; by doing a data quality analysis of the customer journey the data quality team were able to consider ways to improve the current processes. The company started by implementing a full data cleaning and profiling of its existing data storage systems. The profiling reports identified a number of issues. Data cleaning; a simple data cleaning tool was used to update and clean the data. KPIs were assigned to the data quality (known as scoring the data ) so that the teams have an incentive to ensure that data cleaning and update routines are performed regularly. Design of the data entry fields; once the team identified bad data entering the CRM system they were then able to improve the existing data and then, with appropriate design, it was possible to prevent bad data entry in the first place. For example, one of the solutions was to use a third party address look-up to validate the information at point of entry. The process of cleaning and profiling the CRM data highlighted particular data entry sources that were consistently poor. This helped managers to identify staff training needs within their teams. The data cleansing report also enabled the team manager to assign scores to bought data lists in terms of their quality and then rank the list suppliers for value for money.

8 7 Identify issues in the client data journey The team organised the data investigation under these four headings: 1 Descriptive (what happened?) 2 Diagnostic (why did it happen?) 3 Predictive (what could happen?) 4 Prescriptive (how to make change happen) By using this analytics process the company discovered a number of potential sticking points in the data journey. Discrete reporting; the company found that it was a good idea to limit the reporting, to a departmental level at first, to give everyone time to catch up and understand what was required of them. This meant that team managers were able to identify training needs in a constructive way rather than a punitive one. Data quality scores became a regular part of the business reporting to the CEO so it remains a key priority. Once the data quality levels were raised then the management team became more confident in relying on the reporting from that data to start influencing strategic decision making. The customer service team did not ever receive the full customer agreement contract details from the sales teams that meant they weren t able to deal effectively with business customers querying what was and wasn t included in their events package. This issue, first highlighted in the data quality analysis, is now a KPI and compliance is measured in monthly management reports.

9 8 At KETL we suggest We find that helping clients to implement a quick data audit always helps them to highlight big gaps in their data entry. Then we develop reports specific and relevant to each department head to assist them in improving their team s performance at data entry. This process is usually iterative as you fix one problem you will highlight others. Clients often find that as they improve one area of their business reporting and gain a better understanding of the data flow they begin to see other areas for improvement. The business will see how improving the data quality and data capture results in a better understanding of their customers, improved targeting in marketing and more cost effective service delivery. For example, simply removing customer duplicates gives you a true picture of sales volumes per customer, or improving consistency in naming categories from referral sources, gives you a true picture of campaign cost effectiveness. As the data improves the business will become more confident in measuring both the successes and the processes that need improving. The business will start to spot trends and begin to see a direct return on its investment in the data quality programme implementation. Once the is consistently good quality the organisation can then introduce dashboard reporting systems accessible across departments.

10 9 Quick guide to better Get in touch For further information or help with your project speak to Helen to see how we can help > Helen Woodcock Illustration