Why Marketers Should Worry About Data Quality
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The document discusses why data quality is important for marketers. It notes that by 2017, 33% of Fortune 100 organizations will experience an information crisis due to inability to effectively manage enterprise data. The presentation then covers how bad data can enter systems, the impact of poor data quality such as potential 10-30% losses in revenue, and how to improve data quality through profiling and focusing on high impact areas. A case study demonstrates how better data quality allowed a retailer to avoid duplicate marketing and gain insights into customer purchasing patterns.
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Why Marketers Should Worry About Data Quality
- 1. www.ketl.co.uk Why marketers should worry about DQ Bath & Bristol Marketing Network 26th November 2015
- 2. www.ketl.co.uk Ian Cray Helen Woodcock KETL is a data management consultancy. We help clients move, store, report and analyse their data.
- 3. Data is only useful when it is good quality https://www.edq.com/uk/resources/infographics/data-machine/
- 4. by 2017, 33% of Fortune 100 organisations will experience an information crisis, due to their inability to to effectively value, govern and trust their enterprise information. Gartner
- 5. What we will cover tonight What do we mean by data quality (DQ)? How does bad data come about What is the impact of poor DQ How can we improve DQ Case study Take away: profile your data to see what you need to change
- 6. www.ketl.co.uk Data profiling measures 1. Accuracy 2. Completeness 3. Timeliness 4. Validity 5. Consistency 6. Uniqueness
- 7. www.ketl.co.uk How is ‘bad’ data entering our systems? People. Poorly designed data entry fields. Duplicate entries. Multiple data sources. Self-service user entry.
- 8. Experian survey on data accuracy
- 9. www.ketl.co.uk Impact of poor DQ Estimates vary on the impact of bad data on revenue (10 to 30%!). Audit your own revenue losses from poor data. Factor in opportunity costs too.
- 10. www.ketl.co.uk Getting better data. Don’t try ‘big bang’ approach – too daunting. Profile your data. Use familiar datasets that you know you can improve easily. Quick gains.
- 11. You have to start with a very basic idea: data is super messy, and data cleanup will always be literally 80 percent of the work. In other words, data is the problem. DJ Patil, Chief Data Scientist of the White House
- 12. Case study Here’s our scenario. A small online retailer wants to launch a new product onto the market. The retailer buys in customer lists to saturate the target market. Without DQ The company is under time pressure to launch the product so decides to go ahead and use the list unedited. The list did contain a lot of duplicates with different spellings of the same names. The list also contained duplicates of existing customers. With DQ The company decides to profile the bought lists. The lists do contain a lot of duplicates. It fixes the list. The company manages to integrate the ‘clean’ list with it’s existing CRM to further check for duplicates. The company ranks the data list provider according to the data quality it has provided.
- 13. Case study Our retailer wants to try to understand which of its customers are buying what products. Time for some basket analysis. Without DQ The transactional data is given to the marketing analysts on a spreadsheet. The report is difficult to read and there are too many discrepancies in the information to draw concrete conclusions. So the company stick with the gut feeling of the CEO. With DQ The company begins by profiling customer transaction data. This instantly reveals some issues with post code accuracy. Phew! Now the analysts can produce more accurate buying patterns based on demographics.
- 14. Measurable impact of improving DQ Lists: you can start to rank the quality of bought lists to inform future buying decisions. Campaign metrics: you can measure email campaigns for better bounce and open rates. Time: better data will mean less wasted time and effort. Customer service: remember to measure feedback from customers. Linking marketing and customer service data is a key driver for change. Product and or service improvement. Data insight: now you can rely on the accuracy of the information contained in your source systems (e.g. CRM) you can start to report from it with confidence. Better analytics.
- 15. Development: Customer insight Creating campaigns and customer incentive schemes that are tailored will avoid the ‘creepy’ factor. We don’t mind companies using our data when it improves our experience. Really understanding your customer personas, their decision making journeys and whether they are an influencer as well as a high value customer. Customer Service Assistants (CSAs) that have a full transaction history when handling a complaint are more likely to turn the situation around. This is what Amazon does best. Ensuring that the right customer data is available to the right person in your company as soon as it is needed and the information is up to date.
- 16. On the one hand consumers are looking for more tailored and personalised offers, yet are concerned about loss of privacy. We like our brands to know who we are but feel uncomfortable when they know exactly where we are and what we are up to. Shaun Smith, Founder of Smith+CO
- 17. Data enrichment: influence customer behaviour https://www.talend.com/resources/podcast-videocast/overachieving-in-online-retail
- 18. Complexity: single customer view (SCV) • Campaign data • Call centre • POS logs • Direct mail • Credit card data • Loyalty card data • Web enquiry • e-com transaction • Store purchase • Online review • Product catalog • Third party data • Historical data • Social media • Wi-Fi tracking • In-store • CSA • Campaign team • Web design UX • e-Commerce • Real estate • Stock management • Collections • Accounts • Pricing • Sourcing • Logistics Data warehouse, CRM, EPOS, analytics, CMS, Sage, HR, Warehousing, Store operations etc. Customer Business team Source system
- 19. www.ketl.co.uk 13-14 Orchard Street, Bristol BS1 5EH +44 (0)117 905 5323 info@ketl.co.uk @KETL_BI Get in touch For further information or help with your data project speak to Helen to see how we can help > Helen Woodcock LinkedIn: /in/helenwoodcock email: helen@ketl.co.uk
- 20. References and Further Reading Data disasters http://blogs.mazars.com/the-model-auditor/files/2014/01/12-Modelling-Horror-Stories-and-Spreadsheet-Disasters-Mazars-UK.pdf https://www.sas.com/content/dam/SAS/en_us/doc/whitepaper1/bad-data-good-companies-106465.pdf Research on corporate data quality https://www.edq.com/globalassets/uk/papers/global-research-2015_20pp-ext-apr15.pdf https://www.gartner.com/doc/2636315/state-data-quality-current-practices https://www.edq.com/uk/resources/infographics/data-machine/ Cost of data quality http://betanews.com/2015/02/17/why-data-quality-is-essential-to-your-analytics-strategy/ http://www.itbusinessedge.com/interviews/how-to-measure-the-cost-of-data-quality-problems.html http://www.itbusinessedge.com/blogs/integration/what-does-bad-data-cost.html http://techcrunch.com/2015/07/01/enterprises-dont-have-big-data-they-just-have-bad-data/ https://www.experian.com/assets/decision-analytics/white-papers/the%20state%20of%20data%20quality.pdf Single Customer View (SCV) http://www.theretailbulletin.com/news/the_top_three_barriers_to_crosschannel_retail_marketing_and_what_you_need_to_do_about_them_13- 07-15/ Using data to drive and inform sales or help CSAs http://www.datasciencecentral.com/profiles/blogs/data-the-key-to-b2b-marketing-lead-generation?overrideMobileRedirect=1 http://techcrunch.com/2015/07/01/enterprises-dont-have-big-data-they-just-have-bad-data/
Notas do Editor
- Clients big and small – we sell our brains – not software – we use software to make our jobs easier. We want to help clients wit the complex work – can end up either doing the DQ or having to take backward steps to go back and do DQ.
- Garbage in garbage out still holds true. Especially significant for marketers. Gatekeepers of the reputation of a company. Often the first contact point that customers have with a business.
- Customer’s perception of you as a brand is key and its easy for people to go elsewhere – DQ paramount - for each company to decide just how important it is for their brand – measuring impact
- So I will be giving an over view of where we want to go once we get our data in order – insight. Small or large organisation will depend on how much resources you have but principals of DM are still the same. What we want you leave with is a plan and an idea of what is achievable depending on your resources .
- These are the 6 main tenants of DQ. We have produced a more detailed guide to DQ to accompany the presentation as well as our Quick guide to better CRM data. Marketers control the message of a company and they create the first impression a potential customer will have of that company. Marketing’s efforts on accurate segmentation of customers and consistency of message will only be enhanced with the same laser-like focus on the accuracy of the customer data. DQ will enhance the core functions of marketing by informing campaigns with accurate data, by providing a whole customer picture and providing the means to quality test new data entering the CRM.
- Use some examples here. No gender assigned. Mr Charge Dodger. Need to incentivise good data handling/entry. Improve data entry field design. Automate data cleansing routines. Establish KPIs against data quality.
- Impact: don’t forget to consider the opportunity costs. There is also the ‘weariness’ factor in staff. Why both to craft yet another campaign that will reach less then half of the recipients due to incorrect or outdated email addresses. The reputational costs of getting things badly wrong. Customer service issues. Unable to segment properly – not knowing high cost low value and low cost high value customers.
- What is easily achievable in DQ, how and why using KPIs to measure DQ will improve customer insight and add value. Technology has improved a great deal in the last few years and marketers need to know what they can do within their own team and what they will need to get IT to help with. We will use some demonstrations of quick data verification checks to explore what is possible either as batch reporting or in near real-time web integrated data verification look-ups. Depending on the scale and resources of your company you can make a decision about what is achievable within your own team and or within your company.
- Any campaign, any software upgrade project, any new product launch – all will be impacted if you have poor data quality. There is no point investing in data analytics if you can’t be sure about sending out an email campaign without addressing your customer by the right name (Mr Charge dodger) Reputation: Age UK – tidal wave of abuse and drop in income with data protection issues – lack of data cleansing.
- The biggest issue for the rushed job is that they probably aren’t even aware of the resulting issues. The CRM will report on the delivery rates but it might not be able to link the rise in complaints via customer services. The conversion rates for the campaign are poor but this is expected from previous experience so nobody worries. The list provider will stay on the preferred supplier list as the retailer has not extended it’s understanding of the DQ of the list.
- Using transactional data to understand your high value customers, your customers that only ever buy at discount (me) and the customers that buy lots and return most of it. Are there patterns you see forming – Amazon recommends etc. As we see from the ‘with DQ’ list it is possible to start to widen out the value of the customer insight and to get added buy in from other departments. Customer services data really should be informing marketing activity anyway. For retail customers you also have the added data sources relating to customer spending habits via loyalty programmes, customer apps, SM campaigns etc.
- Data insight is about using the information to inform your decision making, not to control it. This stops decisions being taken by one individual – by spending time automating and managing your data in the short term – you gain in the medium term. Companies, even household names, are struggling with this when it comes to analytics. The drive and need is often coming from marketing, we get the importance of our data, we just don’t feel confident about managing it.
- This is something that is very important to our retail clients. The timeliness of data and data reporting. Stock levels, returns, sales in each store, turnover of stock (seasons, sales etc) loyalty cards, online sales etc etc
- DQ and impact of data proctection and security. Eg. Telematics privacy settigs on fleet car tracking to turn off outside of working hours
- Once you have confidence in your data sources you can start to use predictive analytics to help influence even thorny issues such as shopping cart abandonment
- Customer journey technology is not easy. But it is possible in a way now that it wasn’t a few years ago. Customer expectations are far in advance of what most retailers can deliver. Omni-channel nirvana – BUT it is not easy – this is very complex and requires advanced analytics skills and complex system integration. Most of all it needs your data to be managed properly.