Marketing Operations - My Next Adventure

I stumbled into Marketing at a point in my career where I was ready for a change. I had spent five years building large-scale data warehouses and business intelligence systems. During that time I enjoyed speaking with end users and their executives and understanding how the systems would be used. But for the most part, my work didn’t require me to understand the businesses I was supporting. Technology was safe for me at that point, but the constant feedback from every mentor was that I needed to focus on the business aspects of these projects as I progressed up the ranks. Good advice. After years of pure technology focus, I decided to look for roles that bridged the gap between business and tech.

I found a role that required a deep understanding of data warehouse technology, but applied that thinking to the business aspects of marketing databases. Yes, my teams were still building large scale customer databases – one client had 70 million customer accounts – but my personal work was focused on delivering related business process improvement. To accomplish that, I spent most of my time meeting with client side marketing executives and learning about their needs. As I listened, I realized that I had always paid close attention to how companies marketed to me in both a personal and professional context. I was particularly interested in what happened after I gave my name to a company for the first time. How soon did they follow up? Did it appear that they resold my data? Were they respectful of me and did they honor an opt-out when I registered one? I had always loved database marketing; I just hadn’t realized it until then.

In the years following that realization, I built multiple marketing automation systems, learned how to measure the ROI of marketing programs, developed integrated sales and marketing lead generation programs, and managed global customer data quality at a name brand company. Those roles taught me the ins and outs of CRM Marketing, and from there I felt confident that I could take on any role in the database marketing space.

With that in mind, I was excited when I found my new role. I am building a Marketing Operations team from the ground up at a global software company that focuses on oil, chemicals, and pharmaceutical manufacturing. Marketing Operations is defined many ways, but for me it means efficiently managing marketing resources by improving technology, process, and measurement. Woo-hoo! I know how to do that. I’m excited for this new role, and plan to focus upcoming blog posts on establishing robust marketing process, integrating sales and marketing programs, and marketing automation technology. I’m sure I’ll write about other aspects like yearly planning, reporting/analytics, and of course… data quality.

10 Things I Learned at Sales 2.0 Boston

Over the last couple of days I’ve been reflecting on what I learned at Sales 2.0 Boston. I’m sure some of this has already been discussed in other posts, but I’m interested to hear which of these are most interesting to you.

1) Sales teams are demanding integrated Marketing tools to drive their business.

CRM and Web Analytics can no longer stand as separate systems. Polly Sumner described an experience where she never heard back from an unresponsive CIO, but was able to track that the email she sent was opened 17 times. This implicit interest let her know there was an opportunity at that client. Invaluable.

2) SFDC Chatter allows you to identify top performers.

Eric Johnson described the way his team was using the tool to collaborate and identify the company’s top performers. Eric and Polly both shared experiences that the real superstars of the organization shine through when they analyze their chatter follows and participation.

3) Operational Sales Reporting may not be needed in the future.

This was my most revolutionary “Ah-Ha” moment of the conference. If Chatter allows a manager to see who is taking many actions that drive results, and who is unable to interact with valuable leads and customers, then there is no need to track phone usage, leads closed, and account coverage to understand which sales reps are doing their jobs.

4) You can survive without an IT organization!

I was amazed to hear Dave Fitzgerald from Brainshark describe his systematic dismantling of Brainshark’s internal business systems. Dave sounded very proud when he described the 17 core functions that are now implemented by SaaS tools. He also discussed swapping some out that weren’t performing as expected with simple on / off service contracts. Dave is down to three FTEs supporting his whole Sales and Marketing infrastructure.

5) Siebel is not popular among Sales 2.0 folks.

My company continues to use an on-premise install of Siebel as our CRM / SFA. When I told people this, they gave me a look of sympathy mixed with dissappointment. It was like I had just told them that I had a terminal disease.

6) It takes a village of applications to enable Sales 2.0.

As I begin to think about building a SaaS based Sales and Marketing infrastructure, I realize that you need to select a series of tools that each play their role well. At a bare minimum, an enterprise would need a central CRM / SFA system, a Marketing Automation platform, an integrated lead generation engine, a Sales compensation tool, and an analytic package.

7) The table in the back corner at the Hoovers VIP dinner had more fun than my table.

This was evidenced by the fact that we took the “What will you do to fill the lead funnel?” question seriously, and they spent the same time laughing and drinking. And we still lost to Anneke Seeley’s table!

8 - The Sales 2.0 Conference will be changing it’s name to the Sales and Marketing 2.0 Conference in the future.

As Sales and Marketing Alignment panelist, I completely agree with this approach. The best conversations at the conference were about the intersection of Sales, Marketing, Technology, and Data and over time these things will converge.

9) Sales executives get very interested when you talk about advanced analytics.

That was the number one follow up after my Panel. Everyone I spoke thought they could do better than they were today at priortizing leads, and understood that they needed better analytic tools and models.

10) I need to attend more conferences like this one.

I spend most of my team navigating internal company issues rather than thinking about ways to create revolutionary change for my organization. After attending this conference, I am more qualified to help my company succeed over time. There is no better way I can justify a day out of the office.

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?