Introduction. Industry Trends Brief

Transcription

1 Industry Trends Brief Introduction Everywhere we look there are conversations about data: big data, small data, structured data, unstructured data, siloed data. There is so much data about data that it s hard to know exactly what you need to do in order to ensure that the data you have can be leveraged to make better business decisions across the company. Doing so requires getting down into the data-dirt and scrubbing clean the imperfections that lead to bad business decisions. Whether your business goals rest within marketing, sales or IT, the process of sweeping out your old, inaccurate data and sprucing up your reliable data can help ensure that your organization has the foundation necessary to put customer experience is at the forefront of your strategy. This guide, the first in a series on data challenges facing marketers, is designed to do more than give you a bottle of spray cleaner and send you on your way. This guide will give you the support to bring the clean data argument to other team members and business units, the direction to help you focus your efforts and the tactics necessary to make it happen. By using this guide you will be able to simultaneously drive a more effective, coordinated use of internal resources and a stronger customer / brand relationship at the same time. Here is the roadmap for your data-cleaning journey: Why do I need clean data? What data should be evaluated and where do I start? When should I clean my data, once or ongoing? How do I remove the clutter, fill gaps and organize the view of my customer data? 1

2 Why Do I Need to Clean My Data Good, clean data is critical to nearly all of the process and decisions upon which brands are built. The impact of bad data on the organization can result in departmental inefficiencies, poor customer experiences and business strategies built on weak assumptions and inaccurate deductions. In recent years, Experian Data 1 calculated the cost of bad data having a direct impact on 88% of companies with the average company losing 12% of its revenue as a result of wasted marketing spend, wasted resources and wasted staff time. In the same report, damages to customer service capabilities were reported by 28% and reputational damage experienced by 21%. Have you ever talked to a mechanic about an air filter only to hear them say, The AC in your car would run 10X more efficiently if you replace this dirty, old filter? Same idea. Even if dirty data is only hiding in one area of your database or datamart, it can dramatically impact the effectiveness of the company as a whole. While cleaning marketing data, alone, may not solve all the dirty data problems companies experience, the ripple effect of good marketing data should not be underestimated. If your kitchen is clean, then the quality of the food you make there is likely better. When you take that food to the dining room or the living room, the other rooms benefit from the cleanliness of your kitchen. Taking the time to improve the quality of the marketing data can impact the quality of leads that move through to Sales, the records of customer activity and history that are important to Customer Service or even the value of reliability of that customer for Billing. Examining the potential snowball effect of wasted resources is another strong argument for cleaning up marketing data. If you consider the time and money it takes to vet, implement, manage and utilize marketing and business technology, the process can often top hundreds of employee hours and months of time - and that is before paying for the technology itself. IDC recently estimated 2 that by 2018, CMOs will drive $32.3 Billion in marketing technology spend. What happens when we take that shiny-new technology and pour dirty data through the pipes? Marketers and analytics teams begin by deriving inaccurate insights, personas and segments from your data. From this flawed premise, they develop flawed campaigns, built on flawed creative content that pushes out to inaccurate recipients and returns results that are misleading at best. The investment in your technology soon becomes less valuable as a result of your bad data. Clean data is the foundation upon which great brands are built and has an impact on the reliability of the analytics and information upon which all business decisions are made. So for the sake of marketing and the sake of your company, grab your mop and bucket and get ready to clean some data. 1 econsultancy,, March 2014, The Cost of Bad Data: Stat s 2 Forbes, January 2015, IDC Predicts CMOs Will Drive $32.3B in Marketing Technology Spending by

3 Where Do I Start For marketers, the most important type of data to cleanse is customer record data. Unfortunately, with so many channels, touchpoints and devices customer records are often incomplete, inaccurate, duplicate or entirely false. According to SiriusDecisions 3, 25% of the average B2B Marketer s database is inaccurate and 60% of companies have data health considered unreliable. Starting with customer records gives marketers the critical base of information from which they can expand through to the trends and analytics held in big data or unstructured data. Once all the floors in your house are clean, you know you can walk from one room to the other without tracking dirt into other areas. This is why your customer records are so important. If I have a clear view of my customers and prospects, I know who I care about and how to find them. From there, I can take what I know and accurately draw parallels as I look across different types of data to see the larger stories my data might be telling. The most important steps that lead us to that view are removing the clutter, filling the holes and organizing the view. What Do I Do And How Do I Do It? The process for cleaning customer records should be as easy as one, two, three. In order to clean your data you must first remove the clutter, then fill the holes and finally organize the data in a way that helps you start to see a bigger, more important picture. 1. REMOVE THE CLUTTER 3 Sirius Decisions, The Impact of Bad Data on Demand Creation The What Much of the data dirt lies hidden in duplicate, inaccurate or dated customer records. If the proper record is: John Doe john.doe@mail.com 123 Main Street - here are some examples of how the data gets dirty: Duplicate: - John Doe john.doe@mail.com 123 Main Street - John Doe john.doe@mail.com 123 Main St. Inaccurate: - John Doe john.doe@mail.com 132 Main Street Dated: - John Doe john.doe@old_ .com 123 Main Street 3

4 2. PLUG THE HOLES The What The How The How To begin cleaning up this type of data companies often start with hard-key matching logic. This process assigns one key that I can apply to John. Once I have a key set for John, I can associate that key with all instances that look like they are coming from him. Now that you have consolidated and normalized your data, it s time to examine what your data looks like. When you hold up the rows and columns of your data, do you see large gaps and holes? How much of your data looks like Swiss cheese? If you find that 50% of your fields are blank and 25% of your address fields are incomplete, how would you accurately determine whether or direct mail was a more effective technique for engaging your audience? Without clean data, you re sweeping critical insights under the rug, and as a result the ability to perfect your strategy is limited. From there, you get into the realm of fuzzy-key matching logic and custom logic which are confidence-based cleansing techniques. One example of confidencebased logic allows you to assign a 99% confidence rating that the John Doe records in our duplicate scenario above are, in fact, the same record. Given that the only difference is that Street is spelled out in one and abbreviated in another, that would appear a proper amount of confidence and allows us to merge all records with similar levels of variation. Filling the holes means gaining access to the right data in order to get a clear view of the customers in your database. This type of data comes in many forms: public data and private data you can append through 3rd party services. Ask your vendor for match rates to properly gauge ROI for this, and where you may still be left with gaps. Leveraging public data to fill in your database involves the integration of 1st party data (data you already have) and data available through a myriad of publicly available sources like the DMV or a town s real-estate records. Most companies require outside support for this part of the process. 3rd party services can be leveraged not only to fill the remaining gaps, but to add additional fields and layers of clarity that you may not have originally collected. 4

5 3. ORGANIZE THE VIEW The What The How When By 2020, it s projected that the data we collect and copy annually will reach 44 trillion gigabytes and will continue to grow, exponentially, in the years that follow (IDC 4 ). Given the rapid rate at which organizations will need to store and manage data, it s critical to change the mindset around data 4 IDC, The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things Now that you have clean, accurate data, it s time to organize what you are seeing in ways that tell you the proper story. What do you see when you segment your data now? What happens to the story when you start logically grouping individuals who fit certain profiles or personas? What types of analysis are you running and are there trends you should immediately start rolling up to start making better marketing decisions? Example: If I notice that a significant portion of my sales comes from Main Street, where John Doe lives, and that all of the individuals on John Doe s street are 40 year old men working in the sports industry, you know that something is happening there. Doing this requires you to visualize your data in a way that it becomes meaningful. You can t see the larger picture by staring at rows and columns or 1 s and 0 s. Visualization tools help you interpret the data and draw business conclusions from the data. Marketing technology should force the business to think about what data is important for the process of creating customer experiences that lead to sales and how that data needs to be viewed day in and day out. What you start to do with that data takes you into a much larger realm where you start to look at data across the enterprise, examining purchasing patterns and devices used for purchase and web behavior - putting together pieces of the puzzle that might not necessarily live neatly in customer records. As you get into this realm of cross-company data, big data and unstructured data, your ability to understand larger trends and behaviors can be paired with a more firm understanding rooted in your structured customer data; allowing you to develop truly informed and impactful marketing efforts that benefit the entire company. 5

6 cleansing and management. You need to be cleaning your data now. You need to continue to clean your data on a regular basis - forever. Cleaning your data needs to come as naturally as jumping in the shower to wash off the dirt from the previous day. Another way to think of your when aligns with the Agile marketing ideology. Maintaining clean data is like running a two-week sprint on a project that lasts forever. Why? Because as you collect not only new data but new TYPES of data, you will continue to build methodologies for ingesting and analyzing that data. Keeping your data continuously clean takes time, discipline, effort, innovation and hustle - and the reward is well worth the effort. Your Data Is Fresh! So What s Next? Now that you know how to clean your data-house and keep it clean, what else can you do to ensure your organization is set up for marketing success? Keep an eye out for the next topics in our series: Unified Data, Unified Commerce Unite On and Offline Data for a Better Omnichannel Customer Experience Segment Better, Convert More Segmentation Waterfalls Let You Make the Right Offers to the Right People at the Right Time To Boost Conversions Data Blind Spots or Business Opportunities? How to make meaning out of your data to reveal hidden opportunities, blind spots and patterns Predictive Analytics, Proven Revenue Growth How to make meaning out of your data to reveal hidden opportunities, blind spots and patterns About QuickPivot QuickPivot provides fast, powerful, easy to use so<ware for smart marketers with big ideas, but small teams and Bght budgets. We amplify the value of our so<ware with our crew of arbsbc but analybcal marketers who make short work out of the data, integrabon, strategy and producbon boelenecks that hurt so many brands. The QuickPivot plagorm enables marketers, in any industry, to dynamically derive customer insight, listen to brand interacbons and adapt campaigns to create seamless buying experiences. All content Copyright 2016, QuickPivot, Inc. All trademarks are property of their respecbve holders. T: F: info@quickpivot.com 6