Leverage the Contact Washing Machine to deliver Sales Ready Leads
Hi, everyone. Good morning to everyone who is joining us on the west coast and everyone who is on the east coast, a very good afternoon. I would be your host for today’s webinar. I’m gonna just wait for another one minute for all the folks to join in. So, in today’s webinar, we are going to talk about contact washing machine and how such tasks becoming are very critical for everyone in the marketing. So, I just wanna wait for another 1 minute I can see people joining in, very happy to see people coming in and we have at least from 10 different companies like SAP, Cloudwords, Microsoft. So, I am going to start with the presentation. Few good pointers if you are dialing in from your phone. I hope you can hear us else you can simply log on to your desktop and slip the option of calling using your computer only go to the webinar. If you want to tweet to us, you can use an official twitter handle apps @grazitti. If you have any questions during the webinar, you can just type them in the chat box and we will be happy to answer them. We would be sending you the recording on coming Monday and if you need any more information regarding the webinar and the contact washing machine you can just drop us a line at [email protected] Presenting today’s presenters, Rahul Sachdeva, he is data analytics manager and has a vast experience in business analytics and data management. He has worked with companies like Air Canada, Orange and helped them in achieving high ROI. Isha Vohra is web marketer at Grazitti. She has been working with many demands and projects and it was a good idea to bring someone who takes over the demands as per as the marketing operation site. So, in today’s webinar, our focus here is that we would be sharing insights on how biodata can impact your return on marketing investment (ROI) and what are the different ways to categorize your data because mostly, marketers see that there is bad data in their system but we don’t have a categorization of what kind of bad data it is. Then we would be talking about some of the metrics to measure the impact of bad data on our marketing campaigns. Then what are the different types of contact washing machines and then a couple of success stories from the companies who have implemented these contact washing machines and achieved huge results. Quickly talking about who we are and what Grazitti is. So, Grazitti is nearly a 7-year-old company and we have been mainly into marketing strategy, automation, building content both visual as well as rich media then leveraging different technology platforms. Apart from that, we have been working with our customers and business operations and analytics. We have a team of more than 85 people and we have worked with almost 280 clients in the last 7 years. As a company, we bucket our services into 6 main categories. We build communities on different platforms like Jive, Salesforce, Drupal. Then we work with companies in marketing operations that involves lead nurturing, lead scoring and synchronizing Marketo and Salesforce data and setting the processes then, custom salesforce development that includes service cloud, sales cloud, marketing cloud. Then we have been working on many web and mobile development projects including both responsive as well as adaptive designs. Data science wherein we offer custom dashboard and reporting systems for your business and professional services and finally demand generation, wherein we help companies with different marketing campaigns to boost the sales up. Some of our marketing customers, Adobe, GE Healthcare, Marketo, Optimizely, Service Source. Most of our customers are from the west coast and there are few which are from Europe and the east coast. So, I pass the ball to Isha and she is gonna start discussing the cost of biodata and what kind of impact it does create. Thanks, Rachit, so hello everyone. Well to start with, we all know data empowers everything that we do. We collect data all our lives and one day, we get to know that the data we have collected has decayed or has gone bad. This thought is itself scary and you won’t believe what bad data can really cost to your business. First of all, it costs you your valuable time. So, businesses lose approximately 20 thousand $ annually per sales representative on bad leads. Just think about it, not only you pay employees chose 10-time doing non-productive tasks but every bad lead they chase also means less time spent in connecting with potential customers. So, this is actually a negative hit to your businesses productivity and sales. Secondly, it costs your resources. A company averagely —– around $180,000 annually on direct emails sent to undeliverable or incorrect addresses. So, just imagine what kind of loss one undelivered piece of mail would mean to your business marketing budget. Thirdly, it costs you your valuable opportunities. It’s no surprise that inaccurate or incomplete data can result in missed opportunities. To know that why data quality cost U.S. businesses more than $600 billion annually. So, going forward, now let’s know about function wise impact of bad data. According to this demand gen report, around 66% of marketing, 80% of lead generation, 30% of finance and 54% of customer relationship areas are affected by bad data. So, it was found that more than 62% of organizations rely on marketing data that is approximately a quarter incomplete or inaccurate. Now, about considering the impact of bad data on sales and marketing, when it comes to the problem of dirty data in deploying a marketing automation without a solid strategy for both cleaning and maintaining your data will more certainly have damaging side effects on sales as well as marketing. So, if we talk about sales, it is affected by bad data in terms of lost revenue, wasted resources, lower productivity, damage to your credibility and failure of your marketing automation initiatives. On the other hand, marketing is also affected by bad data in terms of delivering the wrong message to the wrong customer annoying with wrong and redundant messages, failing to leverage multi-modal marketing capabilities and distorting campaign success metrics. So, here’s how the common lead flow works. As you can see, the data comes from different lead sources like PPC ads, organic web traffic, social media, partner referrals, list purchase, tradeshows and events, sales cold leads, web listings and portals, content syndication and inbound calls. So this data when enters the marketing automation system where you nurture the leads and qualify them as MQLs. These MQLs are then passed to the sales teams for opportunity creation and then sales teams work on them to mark them as won or lost opportunities. The leads that are lost are again passed to marketing automation system for further nurturing. So here’s what brings bad data to your system. Lead sources like PPC ads, organic web traffic, social media, partner referrals and so on I mean, inbound calls, content syndication and all. So, what are the different types of bad data in an organisation? These include missing data which means the empty fields in a form should always contain data. As you can see here, geographical data is missing in the example would essentially limit this lead to be assigned to a sales rep. Going forward, the second type of bad data is wrong or inaccurate data which is the information that has not been entered correctly or maintained. As you can see, there are junk values in company and email fields. This information captured from a web form is of no use for sales and is basically, inaccurate data. The third type of bad data is inappropriate data which is the data entered in the wrong field. As you can see in this example, the country has been entered in the city field. So, a regional lead will not appear in the regional reports. The fourth type is non-conforming data which is the data that has not been normalized as per the system of records. You can see in this example there are different variations of united states in the country field. So, a marketing segment of all US leads
will not include clients with USA or United States of America in the country field. Then comes the duplicate data which is when a single account, contact, lead etc. that occupies more than one record in the database. This is the example of the duplicate data in which the sales reps have created distinct accounts for a single lead wasting their valuable time, sowing confusion and causing missed opportunities. So, do you have bad data in your system? Let’s see how can you measure the impact of bad data. The first point is duplicate data %. Since the cost of marketing automation is dependent on the number of leads that you have in a system. It is critical that you don’t have any duplicate records. You can measure duplicate data% by dividing the total number of duplicate leads with a total number of known leads. The second is a lead quality score, you can measure the quality of a database by identifying important data values for a lead. This can include fields like job title, country, city, state, postal code, and phone number. By adding weight to each of these schemes, you can measure the quality score of each lead. The third is email deliverability%. The email deliverability% for your marketing campaign is an important indicator of your data quality. As a good practice, you should segment your existing and new data and measure average email deliverability. The fourth is unsubscribe%. The unsubscribe% for your email marketing campaigns also indicates your data quality. Then is email bounce reasons. Also for all the email campaigns, you can measure the soft bounce and hard bounce rate. This can actually help you clearly identify bad records in your system. The MQLs marked as bad data by sales team monitor leads which are marked by sales reps in salesforce as bad data and return to marketing system for clean-up or nurturing. So these are the points which can help you to measure the impact of bad data and now I will hand over to Rahul for telling us about the contact washing machine and how it works to clean up your bad data. Well, thanks, Isha that was really insightful regarding the impact of bad data. It is an accepted fact that data management is an important requirement to be able to run proper segmenting and targeting for marketing campaigns because unclean data effectively reduces the effectiveness of marketing campaigns. Now, moving on what is contact washing machine? Well, we will cover the entire story in next 15-20 minutes. So the contact washing machine comes in 2 versions. One is semi-automatic and the other is fully-automatic. So, the semi-automatic version will cleanse and normalize your data. The campaign integrated with your marketing automation system by implementing a series of smart campaigns for passing leads through data filters that cleanse your data by first normalizing it, washing it through the bad data filters, calculating the lead quality score and finally sending it for lead scoring. As a new lead is created in the system, it would be move through the entire process which will ensure that only a true sales lead enters into the system. Besides, you can also run your legacy data in a batch mode to ensure your clean data stays clean. You can specify the fields you want to cleanse. For example, company name, phone number, email id, lead name and so on. So, to filter the bad data from good data based on the set of rules or algorithms which can be customized according to requirements. A canonical set of rules is applied to all the data to filter out records such as emails like [email protected] etc. So the good data is also passed for normalization. It is through a centralized set of values which scans through the pre-defined fields like country, state, job title and normalizes the values. So, that completes the semi-automatic version. The fully-automatic version not only cleanses and normalizes your data but also enriches your data with external business data sources like ZoomInfo, and so on. All you need to do is to analyze what extra information you want to fetch. For example, revenue, employees, industry, SIC codes and so on. It would take the cleansed and normalized data, scan the companies from the data through external business data sources and fetch out the required information for you in the format that you want. To get output to virtually ‘n’ data source including CSV, Excel databases all can directly to your salesforce objects. So, essentially the contact washing machine performed three portions. First is cleansing, i.e. segregating the bad data from the good data. Second is normalization, i.e. standardizing the data values. And the third and the last one is enrichment that is to external data sources. So, contact washing machine delivers data which is complete as it has got all the required fields, correct that is free of all inappropriate values and consistent. Moreover, it can be seamlessly plugged and integrated with your marketing automation system or your CRM system. So, the point is which CWM do you need? The semi-automatic or the fully-automatic? Well, it actually depends on your requirements. For your ease of accessing the two, we happen together both the versions across each other. So, in seamless integration with marketing automation system is your focus, you may want the semi-automatic one. However, if you want to enrich your data before you do anything else, fully-automatic is the one you should look for. And now for first next couple of case studies, I have companies who have benefited by leveraging the power of contact washing machine. For Alteryx, it processed than more than 200K records processed in a minute that means if you had one million records, you could cleanse them in just 5 minutes. That becomes over 40% quality improvement in the data which resulted in around 37% increase in sales productivity and 25% improvement in lead prioritization and segmentation. And the company Cloudwords was benefited with 95% delivery rate on cold leads. Detailed account information through data enrichment and therefore, better lead scoring and better segmentation and campaign conversions. So, here are the final four key takeaways from today’s webinar. First is integrated data sources. Because every company today uses different platforms and data sources, for example, web sources or CRM systems, marketing automation systems etc. So, you need to integrate the data sources first to collect them in one place. Second is audit your legacy data that is very important. You should audit your legacy data to ensure your clean data always stays clean so that it might not get obsolete. Get sales feedback, you should listen to your sales rep feedback to ensure your data is always updated and finally, integrate CWM to your marketing automation system and CRM systems. So that concludes the webinar about the contact washing machine we would love your feedback, we will be sending out a brief feedback form right after this webinar. It would be great if you can provide us the information with any questions, comments or doubts you have. So, thanks Rahul for everything and he would be taking some questions now. So, can we just put up the questions that we have. The 1st question is how much time does it take to set up a contact washing machine inside Marketo? The actual time depends on the kind of data that you already have and the number of sources from where you are getting all the data. Usually working with Contact Washing Machine you have to ensure that you have the right set of filters which would be normalizing job-titles, city and state fields. So, it mainly depends on case to case basis. The next question is the same question but it is in Marketo. So, I believe in Marketo we don’t have any out of the box functionality for a contact washing machine but using a set of smart campaigns like Rahul mentioned in the semi-automatic contact washing machine wherein we can sit some smart campaigns. So, typically that should not take more than 15-20 hours depending on the kind of filters that you want to set up and if you want to batch run, it depends on the amount of data that you have typically, Marketo allows processing of 50 th
ousand records by hour and if you are on an upgraded server, it can go as high as 100 thousand records an hour. The next question that I have here is from where do we get the filters for the contact washing machine? So, there are some third party sources from where you can fix the filters or we have the list of filters and you can request for and we can send out those to you. And another way to look out at the filters is the kind of bad data that you have in the system. It can be as simple as you can extract all the bad data and sort that out in excel file to see what kind of or what are the common parameters or what are the common values which are making the data bad. The next question is how Alteryx integrates with Marketo? I believe Alteryx is a third party application and it can be set up in the cloud as well as on the desktop machine and it comes with an integration with Marketo wherein you can directly integrate Marketo and it can collect all the records from Marketo, bring them and clean them. Then it can update those records directly and bag inside Marketo. The second question on the same one, yes the data would be going out to Alteryx, so your data would be moving out to Alteryx server it would be getting blended there and then it would be coming back and that’s pretty secure because it would be on a private cloud server. The next question is, is there any connector with contact washing machine to extract from different sources? So, if you are using a fully-automatic contact washing machine which is built on Alteryx. So, Alteryx provides integration with couple of third party platforms and other tools but if you want all the data to stay inside Marketo, so we will have to do some custom integrations like as an example, we recently did an integration where we started gathering the data from the Oracle customer support system, Jive which had the community data then the web portal from where we were getting the sales data and all this was integrated within Marketo and then, we were running it through the contact washing machine to ensure that we clean up the whole data. So, I believe these were all the questions that we have. So, immediately after this webinar I am gonna send feedback form and I would love to hear back on your thoughts and comments and anything you want to know or share your experience with the contact washing machine and I would like to thank you all for joining us today and those a few who could not join, I would be sending out the recording. So, you should expect to hear back from Isha very soon with a recorded video. Thanks again to everyone for joining us. Have a great day. Bye.