CRM Marketing Strategy To Drive Sales Revenue

For the purpose of this post, I’m defining Marketing Strategy to mean the analytics that complement the work done by the Database / CRM Marketing team and the influence that those analytics can have on Sales and Marketing programs. This article isn’t about Brand, Creative, or Media Purchase. It’s also not about Social Media, SEM or SEO. Just to be clear, I’m writing about using Data, Technology, & Analytics to reach out to customers and prospects, improve program results and drive revenue. Generally my recommendations are specific to Email Marketing, but may also be applied to Direct Mail or TeleMarketing.

Marketing Strategy should be the heading under which your company defines it’s analytically based sales programs. But you don’t need a fully developed internal analytics shop to be strategic. In fact, that isn’t the first step at all. First, focus on your data. It doesn’t matter how good your analysts are, they won’t be effective or efficient if you give them low quality data to work with.

So, let’s begin with the data. The following three areas of Marketing Strategy rely on Data Governance, so for my opinions there read up on Lightweight Data Governance.

Opportunity Analysis

If your data isn’t clean, you can’t get a feel for how well you are doing. For instance, duplicates in your data when analyzed in aggregate cause double-counting within buckets, double-counting across buckets, and general noise that diminishes the value of analytics and modeling. If you have the same company listed as both a customer and a prospect, and they both fall in the same industry or geography, your analytics driven programs will fail.

The solution is to develop a source system down approach to gathering data for your Marketing Strategy work. If you collect the data with reporting and analysis in mind, downstream processes will benefit. So work with your Sales, Service and Finance teams. It will take them an extra few seconds to correct the problem. Every customer facing employee should spend a few seconds each time they speak with a customer to ask, “Is our contact information for you still correct?” That change, which may require a culture shift that starts with the executive team, will dramatically improve program results.

Targeting and Personalization

Do you use Marketing Strategy and leverage data to drive campaign results? Test and Learn is a sneaky term for Marketing Strategy. It sounds less expensive, so use it.

Your content should be specific to your target’s business problem. It should be tailored to their purchase history, or lack thereof, and include next (or first) best product or cross-sell specific messaging. You should personalize the email to the targets name, title, company name, and other fields in your CRM system. If you have analyzed the lift generated by personalized vs. non-personalized messaging, you understand the need to target your emails. 

When you segment targets and personalize the subject line and body content of your emails, your open and click-through rates will increase significantly. Also, your opt-out rates will go down. The quality of these key fields must be monitored and improved to take advantage of that lift.

Compliance and Customer Interaction

If you are not familiar with CAN-SPAM, read my take on your responsibilities as an email marketer. You are required by law to do a great job at managing email opt-outs and confirming that your business is transparently represented in your email marketing. Even forgetting about email compliance, you should still consider the customer impact of bad marketing. “Mark Goloboy or Current Resident” = Trash Can. Trash Can = Poor return on direct mail spend. Read: Not enough revenue to justify your program.

So how do you mature your processes quickly to take advantage of the potential sales impact of Marketing Strategy? You must develop quality processes that remove bounced emails from your lists and opt-out all instances of an email address when requested. You also need to be sure your sales reps are honoring your opt-outs. Lastly, you must review your data for patterns of Sales or Service rep entered garbage. What’s the most frequently occurring word in the First Name field of your contact records? I bet it’s not “John.”

Data Acquisition

Whether you are in B2C or B2B Marketing, there is value in purchasing reference data for your existing customers, and lead data for prospecting. How much value is there? You need to test purchases from different vendors to determine that. There’s no other way. Buy a small list. Or better, ask a list vendor for a free list to test. They aren’t selling a lot now. Take advantage! Then predict ROI by scaling the results and associated spend across your whole universe of customer and prospect data.

You can also measure the impact of cleaning, de-duplicating and referencing your data to a standard set by testing those processes against a statistically significant sample of your customer data. Again, you may be able to get some of these services for free by getting into a competitive sales situation. Your prospects ask you for this, right? Mastering this customer data requires assistance and cooperation from Sales, Marketing, Customer Service and Technology, which means Data Governance.

Demographic vs. Firmagraphic Appends

Note: This is a continuation of the B2B vs. B2C series. I will also be presenting some related material at the MIT Information Quality Symposium next week (MITIQS 7/15-7/17 2009). If you are attending, please introduce yourself.

Data Acquisition is an important part of any data driven Sales and Marketing program. I associate the append process with Marketing since that’s where these programs are generally funded, but the clear beneficiary of the work is the Sales organization. And of course, it takes a partnership between Technology, Sales and Marketing to make data acquisition programs successful. A data file on a shared drive or standalone table in a database has little or no value. The data has to be integrated with customer and prospect lists and loaded into the CRM and SFA systems and / or presented through your BI/DW tools to show ROI. Sorry… I got on a roll with the acronyms. They’re defined at the bottom. I’m in a Data Governance role now. Acronyms and definitions are my life (in a scary, scary way).

This raises the topics of Data Cleansing, Address Standardization, Merging & Matching, Deduplication, Archiving, etc. I’ll save those topics for future posts, but would encourage questions in the comments section. It’s fair to say that without a Data Quality focus you won’t achieve the optimal results for either Demographic or Firmagraphic Data Acquisition program.

Demographic Appends

Demographic data is generally used by Marketers and Analysts to determine the best way to target a consumer base. Starting with a universe of customers or prospects, a company can buy information on:

• Credit History
• Purchasing Patterns
• Housing Location and Situation, e.g. owner, renter
• Salary
• Lifestyle / Family

You can also buy data that segments customers based on the provider’s best information. Some is based on geographic of financial information. Others are based in buying patterns. Sometimes they even have cool segment titles like “Boomer Barons,” “True Blues,” and “Jumbo Families.” (All of those are from the Acxiom Personicx family of pre-defined segments). The goal of a demographic append is really bulk segmentation. How do I get enough information about my consumers to segment them for marketing and analysis. In most cases, the purchaser doesn’t care about any single individual or household, other than to get them into the correct program. The marketers are looking at sets of consumers, and then targeting from there.

In very high end consumer sales, e.g., luxury items, high net worth banking etc, you may find cases where sales people will use the appended data at the individual level. But generally it’s used for grouping customers into buckets, finding buying patterns and trends across like customers, and performing analyses such as next best product, lifetime value, and similar. Consumer sales is so transactional, that there isn’t time to research before any one conversation.

Firmagraphic Appends

B2B data, which I prefer to call “Firmagraphic” but have also seen called Firmographic or Firma-graphic, can provide value for not only segmentation, but also for pre-call research. B2B Sales is moving toward a more consultative approach where the sales rep becomes more of a partner with the purchaser. The best sales reps do this across all businesses. Rather than try to sell one product or service (transactional), the sales rep tries to understand the company’s need and deliver a suite of prodcuts & services and sometimes even the workers to use them (in an outsourcing arrangement). I’m sure if you’re reading this you’ve seen the varying styles of transactional and consultative sales reps.

To arm those consultative sales reps with the appropriate information, companies often purchase firmagraphic data and load it into their CRM systems. This data may be grouped into:

• Parent Child Relationships
• Locations of Related Companies
• Contact Information for Executives
• Industry Codes
• Number of Employees
• Revenue

This allows the rep to quickly look at the companie’s situation, and taylor their initial pitch accordingly. “I see you’re in a fast moving industry, with over 100 employees, and that you’re company has decentralized offices across the country. Have you heard of Product A that might meet your needs?” That conversation can only happen to a perspective customer if a firmagraphic append has happened previously. Which reminds me that another form of firmagraphic purchase is the ubiquitous, “Get 200 leads in your target industry,” but that’s not what I’m talking about here.

Segmentation of B2B customers is also sometimes based on Firmagraphic appends,  but as mentioned in the original B2B vs. B2C post, householding in B2B is focused on creating parent child relationships among your customers and prospects. You should also use all of your existing customer information including location, purchasing and servicing history, and past communication response to segment your customers. Once you have those relationships built, you can begin to analyze your coverage of headquarters and branches based on firmagraphic data.

Analytics then focuses on similar models for next best product, lifetime value, likelihood to purchase, likelihood to respond to certain campaigns and others. The tools to d

Acronym Glossary

CRM: Customer Relationship Management. Generally refers to the systems used by Sales and Marketing teams to store and organize customer contact information, purchasing, and servicing history. CRM systems also pump out lots of data used for operational reporting and as inputs to customer analysis and segmentation.

SFA: Sales Force Automation. Tools used by Sales Reps to manage their actitivities. This would include follow ups/reminders, appointments, leads, renewals, etc.  SFA systemsalso includes the operational reporting of those activities in some cases.

BI: Business Intelligence. The analytics and reporting tools built on top of the Data Warehouse. Business Intelligence can also be used to describe the practice of analyzing data to determine important insights and drive strategy. These tools look across CRM, SFA, manufacturing and financial systems.

B2B: Business to Business. Marketing and Sales activities along with associated products and services targeted at business customers.

B2C: Business to Consumer. Marketing and Sales activities along with associated products and services targeted at individuals or households of consumers.

ROI: Return on Investment. A simple calcualtion that takes the difference between the cost of a program and the returned value from it, and divides it by the cost of the program. So if I spend $100 and make $150, my ROI would be the $50 of benefit over the $100 of cost: .5 ROI. People often forget to subtract the cost from the numerator and inflate their ROIs.

Upcoming Blog Posts

June has been a light month for blog posts. I’ve settled on upcoming topics, and even drafted my next couple of posts out. Why the slow month? I took a detour into Lean BI research that proved uninteresting to write about. I’ll scrap that. Gone also is my brief foray into Cloud Computing for BI. I’m just not an expert there. Good to learn about, but nothing I want to associate my name with yet. I also had to knock out a conference presentation for this Summer.

I’ve settled on the following upcoming topics. Bear with me while I get them out there. Enjoy the previous posts in the Data Governance folder. Also, please comment if there are any areas you’d like me to explore.

1) Lightweight Data Governance: A Starting Point

A continuation of the previous post. I’ve finished my MIT Information Quality Symposium presentation for this Summer, and think it would be helpful to write some background on the theory I’m developing. In a recent conversation with a Data Governance colleague, they referred to the work as “different from what everyone else is doing.” I hope that’s a good thing. Either way, it confirmed that the presentation will be provocative for that group. So I’ve got that going for me.

2) Data Quality and Data Governance Blogs I’d Recommend

I’ve been keeping a list of those bloggers I think are really good. I look for people who put their opinions out there, and keep the topic light. I’m also a fan of those who are tool agnostic. Too many in our field are married to their vendors. It’s a bad position to take.

3) Demographic and Firmagraphic Appends

A continuation of my B2C vs. B2B series. In this post I’ll explore where the value is, how to incorporate and whether you should even bother. Have you maximized your own data first? Are there other ways to get access to this data for free? Some podcaster’s I follow think so. More to come in the blog post.

Kaushik’s 10/90 Rule applied to B2B Data

Avinash Kaushik is the leading expert on Web Analytics basics. In his book Web Analytics: An Hour a Day he describes the baseline understanding you need to build and maintain an analytics driven website. I would highly recommend the read. In one section, Avinash describes his 10/90 rule, which he has also posted on his blog, Occam’s Razor. The shorthand version is Kaushik recommends spending 10% on tools and 90% on intelligently trained people to get the most return from your analytics investments.

How else can the 10/90 rule be applied? What about B2B Data? Are you getting the most value from the lifeblood of your outbound customer acquisition strategy? Are you paying too much for leads and focusing on quantity rather than quality. I know I’ve fallen into that trap.

Data Acquisition of B2B Leads

When planning your budget for Data Acquisition, you need to consider not only your list spend, but also the analysts empowered to develop your Leads Strategy. It’s easy to buy a 10MM row list of B2B Leads. All you need is money and a list broker to sell it to you. The more mature model is to understand your customer base and only buy prospect information for companies who have a high likelihood to purchase your product.

My title was previously “Director of Marketing Analytics.” I’m reminded of this daily when vendors call and leave messages for Mark Goloboy, Director of Marketing Analytics. My contact information was gathered from website and magazine subscriptions, webinars attended, and likely my LinkedIn profile. I’m on Jigsaw, Pipl, and ZoomInfo. Those companies gather and sell your data. That’s what they do. If you’re reading this, you are likely on several large aggregated lists that are built from dozens of smaller niche lists. I would recommend googling yourself and opting out of any services that look like aggregators, unless you enjoy hearing vendors’ sales pitches.

But wait. I buy list data. And you don’t want to hear from me just like I didn’t want to hear from you. And therein lies the problem. A vast majority of the leads you can purchase don’t want what you’re selling. No matter how good your sales people are, most of the leads you can provide them will never be sold. So how can you improve results? You need to understand your customer base through analytics, develop patterns of purchasing customers, and only buy those leads who have a high likelihood to purchase from you. That’s easy to say in two sentences, but it requires business analysts with deep understanding of your company and industry, statisticians who know your customer data and can transform it into insightful scoring analyses, and sales and marketing strategists who know how to work with your front line sales people and deliver complimentary messages to your customers.

The 10/90 Rule Applied

So to avoid overpaying for unfiltered B2B lists, you need to follow Kaushik’s rule. If you’re planning to pay $10 for data then you need to assume $90 worth of analytic commitment. That needs to be applied at four different stages.

1) You need to commit to analytics before the list purchase to determine which prospects are likely to become customers. Then you can avoid buying full data sets and instead buy targeted niche lists. Also at this stage you should develop program revenue projections and determine if the list is worth the purchase price. If not. Don’t buy it! Spend the money elsewhere.

2) Once you have the list in house, you need to score it based on previous buying patterns, filter out any existing customers, and prepare the list for distribution to your sales teams.

3) During the program you need to analyze results quickly and course correct if the campaign is not producing results.

4) Following the program you should analyze what worked and what didn’t. Did your models and projections hold up? What was the actual ROI? Was it better for some segments? All of these questions need to drive future programs.

And don’t forget…

Throughout the process, your analysts need to coordinate with Marketing and Sales leaders, CRM and SFA systems owners, and financial strategists to align with other programs and schedules. Following the process communicate with all constituents. Let them know where you’ve had success, and MORE IMPORTANTLY let them know where you can do better. You build credibility if you point out areas of improvement and show the ability to mature your programs.

CRM Data Quality for Sales and Marketing

This Summer I’m presenting CRM Data Quality for Sales and Marketing at the MIT Information Quality Symposium. I’ll post more on that in the future. As I was preparing my slides for the presentation, I remembered an exchange that led to the following blog quote. Thanks to Marci Reynolds, a former colleague, for consulting me when writing an article about Sales Data Quality. Marci and I collaborated on several projects and sales programs over the last few years and data quality was always a hot topic.

From Marci’s Sales Operations Blog

Mark Goloboy, Director of Global Data Governance for Monster.com recently shared, “ Poor data quality is a symptom of other issues. Organizations must focus on upstream data quality and correct business process and systems issues to succeed. Inconsistent information is the reason why many sales initiatives fail. One bad lead can cause a rep to ignore a whole program.”

Data quality needs to be a primary consideration in any effort, and should be discussed throughout. No dataset is perfect. Every lead has a probability to be bad, and every constituent is looking for an excuse if their portion of the project goes wrong. Manage data quality expectations and communicate issues to invalidate the excuses. A former boss would say, “take those bullets out of their guns.” That’s the best way to get results.

There is no perfect answer when confronted with data quality questions, so planning is key. Here are some common excuses to prepare for:

These leads stink. They are ALL DUPS.

No lead list is perfect. You should expect 5-20% of the leads in any list to be bad. Work with your vendors to have them help reduce these numbers. Test smaller subsets, and negotiate prices based on the percentage that are unusable. Arrange for “net” agreements based on your existing lists. Also, duplicate leads are often caused by poor data quality in the source system. When buying or gathering new leads be sure you have a strategy for matching to your CRM system, but also a plan to pilot usage of the leads to get feedback early and course-correct.

Your numbers must be wrong. They don’t match mine.

If during a program one report doesn’t match another then further research is needed. For what purpose was each report built? What system or reporting tool did the data come from? These questions could expose underlying issues and incorrect data usage. One caution… don’t assume your report is “correct.” You could miss a larger problem.

It will take weeks to review that data before it is usable.

This is a symptom of poor strategy. Matching and data cleansing needs to be automatically processed. You’re doing it wrong if you have resources manually reviewing large sets of data. You either need to mature internal processes or work with vendors who can help you do the matching externally. This needs to be built into the program cost, and should justify the program from an ROI perspective.