Boston Data, Technology & Analytics Blog by Mark Goloboy

Sales and Marketing Alignment Series Intro

I will be joining a panel discussion on June 28, 2010 at the Sales 2.0 Boston Conference. The topic for my panel is Sales and Marketing Alignment, so for the next couple of weeks I’ll be writing a series of posts on related theory. To cover the subject, I will be scratching the surface of very complex subject matter. Please let me know if you have any questions or would like me to dive deeper into any topic.

Here’s my initial list of topics, although these could certainly change as I go. 

1. Intro and What is Sales 2.0
2. CRM and Marketing Measurement to Drive Sales
3. What is a Qualified Lead?
4. Systems Integration - Connecting Web, CRM, and SFA tools
5. Global Sales and Marketing

This is a departure from previous posts about Data Governance, Customer Intelligence, etc., but it’s what I’ve been thinking about. I hope you enjoy.

What is Sales 2.0?

Most of the information about Sales 2.0 is from companies affiliated with the conference. That makes sense as this is about the intersection of Sales and Marketing, and these companies are eating their own dog food (my favorite cliche from my consulting days). It may be that there are lots of other people writing about it as well, but it’s no surprise that the content Google is finding first is from these Marketing focused companies.

What the hell is Sales 2.0? I’m not the first person to ask that question. HubSpot, a conference participant, asks that very question on their blog and their guest writer, Nigel Edelshain claims to have coined the phrase. N. B. I have no reason to doubt him, I’m just excited how nicely the Google results are shaping my blog post. If that’s intentional from the marketing spend of these companies, they are doing a great job of guiding my understanding of a new topic. It’s good to think about these things when approaching advanced Sales and Marketing techniques, because your ultimate goal should be to replicate this approach.

Nigel defines Sales 2.0 thusly: “Sales 2.0 is about sales people using Web 2.0 tools and social media to sell more effectively.” Alright. I get that. A good standard definition that everyone can buy into. I think it may overlook some of the CRM plumbing that makes this concept functional, but I like how concise that is.

What about others? How do the vendors promoting the theory define it? Inside View, who is another conference sponsor, has a whole page dedicated to defining Sales 2.0. They have been supporting the conference and concept since 2007 and confim Nigel as the creator of the original concept. Their definitions page links to other definition pages, and I count at least 30 different definitions for Sales 2.0.

I’ll stop there, but continue soon with CRM and Marketing Measurement. I look forward to your comments.

B2B Customer Intelligence - Flexibility

This is the second post in a new Customer Intelligence blog series. Please add comments if you’d like me to expand on any of the points, or have suggestions for other topics. I also welcome any feedback on the content.

Customer Intelligence takes a lot of work. To implement it properly you or your company will need to invest time and resources on technology, data quality and ETL projects. You will also need to hire or contract with analysts who will help to derive the insights needed to properly target your best customers and prospects.

As soon as you’ve invested all of that money, the target will change. Sales goals are redefined yearly at most companies, and even the most stable change companies their approach every couple of years. The Customer Intelligence program you’ve defined needs to flex with your corporate goals to be effective. Rather than build a rigid model that’s engrained into your production systems, a flexible model will allow for longer term success and save money.

Strategic Flexibility

From your first brainstorming session on Customer Intelligence, you need to think about how the program can meet multiple corporate goals. Let’s say that today your company is dedicated to growing it’s customer base through prospect acquisition. If you’re successful at that approach, your company will likely mature into a customer retention focus over time. Seems logical, right? So from the start how do you create prospect scoring approach that will also translate into customer lifetime value models. Which factors are shared in determining prospect conversion and customer longevity? If you were the CEO, which direction would you take the company in next? Now ask others in the brainstorming session what they would do. Set 3 or 4 targets and be sure that your customer intelligence plan can support all of them with little adaptation.

Insourced vs. Outsourced Analysts

One of the first decisions you will need to make is who is going to do the work. I assume if you are reading this that you’ve got a solid background in data quality, project management, and a few other relevant skills that will vary from reader to reader. Are you a PHD statistician? I’m not. Are you willing to spend 80 hours next week writing ETL code? Having done that for a few years (anyone else remember clicking “run” at 2AM on a 4 hour denormalization package, then cuddling up under their desk for a nap?) I’m happy to hand that work to someone else. You will need support, and it will be expensive.

Depending on the size of your Customer Intelligence program and the size of your company, you will need to decide on the best approach. Personally, I’ve used both internally staffed and external analyst firms and I’ve had success and failure with both. I’ve found the key is to have more than one modeler working on the problem to share ideas and help you keep track of the other. There’s no bigger wake-up call than hearing from a statistician that their peer is incapable of doing the work assigned. This stuff is really hard. Not everyone who has a good resume can build a model from scratch that drives your business to the next level.

To stay flexible, you need to be able to grow your team quickly during development and then shed excess analysts while Customer Intelligence programs prove their success. I would recommend a hybrid aprpoach with some full-time employees to maintain models and external consultants available when new models are needed or when your internal folks hit roadblocks.

Developing a Library of Models

Another key to Customer Intelligence flexibility is to build your models on top of a standard data set that can be used over and over again. There are nuances to using corporate data. It’s time consuming to build a dataset of input variables for a statistical model. There are issues based on business definition, geography, time calculations, and data sparsity that all require both business analysis and technical work to get right. By building a single set of denormalized data (think one big table where the transactional keys have been resolved to their text values in the data set) you will save time and money the next time you try to build a new model. Of course you can add variables over time, and even include different versions of the same values. But by building it once and using it repeatedly you will get more results with less effort.

B2B Customer Intelligence Series - Introduction

My job title and primary focus is Data Governance, however the data I spend the most time managing is B2B data. As a former consultant, I constantly find myself suggesting new applications to drive Sales and Marketing programs. Sometimes it’s as basic as dipping into database reserves to find new prospects, but often my extracurricular projects are pure Customer Intelligence, also referred to as CI. Customer Intelligence can cover a lot of ground, so let me define it.

Customer Intelligence is the intersection of Sales, Marketing, and Analytics that helps present the best customers and prospects to inform company strategy and tactical approach.

Customer Intelligence work manifests itself as Analytic projects including predictive modeling, cross-functional leads programs, and Sales and Marketing strategy projects. In most companies I’ve worked with, Customer Intelligence is distributed across several functions and that group collectively defines the companies Customer Relationship approach. In others a central group focuses on Customer Intelligence and coordinates the distribution of related information to drive strategy.

Other companies have no Customer Intelligence. They are Customer Ignorant. In that case, the Sales and Marketing teams approach customers based on generic approaches and anecdotal history of which customers are the “best”. With good products and excellent customer service, this approach can work. However, over time it will open the door for competitors to take over and dominate the market.

Here are a few thoughts for the upcoming series. The plan is for each of these topics to be developed as an entire post over the next few weeks. If you have other suggestions, please comment on the post or tweet a reply to @markgoloboy.

1) Customer Intelligence Flexibility

No matter how successful it is, every sales strategy will be retired someday. It may be next week. It may be next year. But be sure, the target will be set somewhere other than it is today. How do you build a Customer Intelligence strategy that allows your organization to stay agile?

2) Aligning Against the Opportunity

Once properly implemented your Customer Intelligence approach should become an integral part of the sales reps’ day. How do you integrate the information with your existing processes and systems to align against the opportunity?

3) Direct Marketing Impact on Sales

Customer Intelligence can help define a set of high value prospects that deliver return on Marketing investment. Determining which factors influence purchasing requires advanced analytics. What’s the best way to develop analytic models and measure whether they predict a prospect’s likelihood to become a customer?

4) Defining Lifetime Value

Determining Lifetime Value (LTV) of a customer and potential LTV of a prospect can align Sales, Marketing and Product goals with corporate strategy. However, LTV analysis requires that your product and sales data has been collected consistently for your company’s history. How do you develop and use LTV to drive product direction and Sales and Marketing focus?

Data Governance for the Executive Level

You’ve done your work. You understand the issues. This is your one chance, so it better be smooth.

My previous blog posts have focused on Lightweight Data Governance for the most part. I’ve sprinkled in some more fomal theory that I’ve learned from the experienced pros, but for the most part I’m writing about my own experience with Data Governance. If I sat here and told you the best way to manage a mature interdepartmental Data Governance practice, you’d call B.S. And you’d be justified in doing so.

With that backdrop, I’m going to begin to describe the transition from Lightweight to Formal practice. I don’t yet know where I’ll end up, but along the way I’ll try to help others with their journey. At some point you will find the limit of project based, department level Data Governance. Whether it’s funding for resources and tools, or interdepartmental coordination, you will need to present Data Governace to a room full of executives.

Do you have a mature presentation ready at a moments notice?

You never know when you’re going to be asked to present. Your boss may say something in a meeting and the next day you get your opportunity. You should create a short 3 or 4 slide presentation that quickly justifies the work your team does. It should be provocative, show the problems and your solutions to them, and clearly demonstrate the value your team represents to the company.

The slides should include:

1. Your Company’s Problem and how Data Governance can solve it
2. Your Current Work Plan, which should be High Level and written in business terms
3. Your roadmap for the next 12 - 24 months
4. Challenges (funding, resources, roadblocks) and your solution to them

Do you have sponsors who can describe your value?

If you are the only one who believes your work is necessary, then it’s not. You must build relationships with the teams you work with, and build credibility with their management over time. If you were called into a meeting right now, which three executives one or two levels above you would you invite? Who would be invited by the organizer. They should understand how your work benefits them, and be willing to stand up for you.

Get right to the point.

Why do the executives need to spend this 30 or 60 minutes listening to your presentation? Think about it from their perspective. They have much better things to work on than this, right? Tell them why it matters up front. Make it about revenue potential, solutions to business (not data or technology) problems, or cost savings. That’s the way an executive thinks. You can also talk about control, compliance, and the corporate maturity that your work representes. But, trust me, focus on the dollars and business problems first.

How can Data Governance increase revenue?

This one is especially important during this terrible economy. What’s your company’s #1 goal this year? Sales. Nothing else matters if there’s no money coming in the door. How can Data Governance help the Sales team?

1. Clean up customer data so the reps know what to focus on. This will require improved systems, better processes for reps, Finance / Order Administration, Customer Service, and anyone else who touches customer data. That interdepartmental coordination requires Data Governance to understand the issues caused by poor data quality.
2. Improving the data will remove inneficient admin tasks from your Sales Rep’s day, allowing them to focus on selling more. If the Rep needs to sift through old prospects that will never purchase to find the hidden gems, they are not working efficiently. Data Governance should develop the processes to maintain the Sales reps portfolio systematically so they have fresh data to work with. Obviously, this is more important as your Sales organization and customer base grows. If you have 100s or even 1000s of customers, you can probably ignore this one.
3. Allow management to focus on the issue, and not the noise. Every time a Sales rep sees a bad row of data, they either move on, cringe and move on, or scream about it. The ones who move on quickly make the most money. The ones who scream are looking for excuses not to sell. They will complain to their management, who will invariably complain to those responsible for the data. Is that you? By removing the excuse that the data is bad, management can focus on the real issues of Sales productivity.

What business problem problem are you solving?

I think the fun part of Data Governance is that it allows you to help resolve longstanding business problems and answer tough questions. If that’s the result of your early data governance work, you’ll get funding to do more. What is the direct business impact caused by inconsistent, incorrect, or misleading data permeating your organization? Who screams about it in meetings? Go ask them how you can help. When you understand their business problem you will know where to start.

Lastly, how can it reduce costs?

The most important part of that question is “Lastly.” Everyone else starts there, but I think it’s the hardest to sell to your management. A revenue or business problem based justication is more strategic than a cost savings plan. Anyone can save costs. Cutting resources or choosing different tools is easy. Look around. Your management has done it repeatedly this year.

NYC Data Governance Trillium Seminar

Earlier this month, I presented to the Trillium User Seminar in New York City. This post sat as a draft while I worked on some fun executive level projects. More on that in the future. Thanks to Mike O’Connor from Trillium for the invitation to speak. For any remaining non-believers, Mike found me through Twitter and this blog. I was also honored to be on the same agenda as Jim Orr. I’ve attended several of Jim’s Data Governance seminars and appreciate that he’s able to leverage his experience into credible, expert recommendations.

I presented a Data Governance Approach that included a description of how I started my Data Governance program, the Lightweight Data Governance theory, Benefits to Sales and Marketing, and Data Governance in a Down Economy. Most of that theory is available in other articles in the Data Governance folder, but if you’d like me to dive deeper into any of them, please comment on this post.

The seminar included about 30 attendees and the level of Data Governance expertise was wide ranging. Regardless of experience, the group asked great questions that focused on two areas: Where do I start, and how do I present this to people who don’t understand Data Governance? Keep an eye out for another post on Presenting Data Governance to the Executive level for answers. It was also fun to discuss a wide range of Data Quality issues including globalization, bridging organizational silos, and which data matters most. Fun stuff. More to come.