Think of the times in life when you are barraged with a tidal wave of information, but all you really want to know is, “what do I really need to know?” That inch-thick stack of mortgage paperwork. Credit card agreements. Mobile phone instruction booklets. Automobile owner manuals. Insurance policies.
Now let’s picture the documents that tell you only what you need to know. Recipes, for instance. They tell you: Get these specific things out of the cupboard or fridge … if you don’t have some items, buy them … and this is how much you need … now do these actions in this order. Recipes walk you through only the actions needed to get the desired result.
Recipes do not tell us all information there is to know about cooking and baking and the foods we’re making. There is a reason why measurements should be precise when baking. There is a science to proper substitutions. There is a method behind why some foods are ideal paired together and others should never touch each other on the same plate. But a recipe doesn’t get into all that. If you want more information, there are plenty of books and Internet sites where you can learn.
CRM contains an entire library of information about customers and sales. But you don’t need the whole library all the time. Isn’t it counterproductive to walk into a library and face stacks and stacks of books when all you want is a book about how to train a new puppy? No one just wanders the shelves containing millions of pages of information if they’re seeking a specific solution and they need it fast. Instead, there’s a simple search method that gives a few choices, and it gives the brief information needed — in this case, a code of letters and numbers – to quickly and easily find the location of the right book.
Our vision is to do this for CRM. To deliver only the information that sales reps need each day to take action. To deliver only the most relevant information, e.g. the information that leads directly to actions that will produce sales and revenue and keep profitable customers happy.
We believe that less information is often more valuable.
So, how much information — and what kind of information – are your sales reps receiving? How can you make it actionable information, more like a recipe?
Sourcing new outbound leads is a never-ending endeavor for sales and marketing. Prophesies of cold calling being dead have not come true if only for the simple reason that prospecting through all channels must be on the table to fuel the engine of sales growth.
Companies typically procure lead data from four main types of leads:
Internally-generatedleads: Referrals, word of mouth, events, etc.
Intelligence-based leads: Newsfeeds, industry alerts, personnel changes, etc. InsideView is one good example of this, but we believe LinkedIn also fits this mold (the evolution of this is exciting)
Special or vertical lists: Trade associations, commerce groups, organizations operating with a geographic charter
Compiled lists: The likes of D&B and InfoGroup, including credit files
But as any sales or marketing manager knows, simply dumping more records on a sales rep is a thing of the past. Lead nurturing and scoring are the norm, wherein prospects are nurtured until they raise their hands as hot leads, and are then forwarded to sales. In addition, predictive modeling that identifies the most likely prospects based on the “ideal customer” profile must be part of the mix.
It’s clear that the prospects at the top of the funnel – the ones you might pay for – are not sales ready. Further criteria and filters must be added which reduce the number of truly viable sales-worthy leads. Let’s take a look at how the leads get whittled down in the pipeline – we call this the “prospect waterfall:”
The net result is, if you are paying for leads and then “throwing away” 55% of the records (typical from our experience), your true list cost goes up by almost 125%. Add the additional processing cost, sales rep fatigue and opportunity costs, and you can see why your prospecting results and ROI are not what you had hoped for!
For best ROI, pay only for what you use.
If your current processes throw away prospect records that you paid for before the sales reps see them, it is time to reassess your lead generation program. Or as one of our clients put it, “stop buying leads until you figure it out.”
Our CRM trends to watch in 2011 were among the most-read words here, all year. Now let’s look forward to what’s in store for sales and marketing data in 2012 …
FUSION OF SFA WITH EMA = TRUE CRM:
With continuing innovation, sales force automation systems (SFA) have been transformed into a sales rep’s best friend, as discussed in an insightful blog post at Software Advice: easier implementation, data accessibility and now the benefits of analytics and marketing automation are aiding the success of sales teams using these systems.
The success of CRM and Marketing Automation is no secret. More B2B organizations will take advantage of this profitable alliance to create a true lead generation life cycle platform, so that the handoffs throughout the prospect -> lead -> nurture -> sale pipeline will become more seamless and accountable. To accomplish this, data, analytics and best practices will play an integral part in relevant communication.
The customer value equation will go further so companies and sales teams can generate more revenue and profit from existing customers. This means examining every aspect of customer value, determining where it will come from and coaching/training to empower sales teams with the appropriate tools to realize such value.
CUSTOMER OWNERSHIP:
With relationships becoming increasingly more mobile and social (and perhaps personal too), there will be contention on who actually owns the customer: is it the rep, the company or the data/app provider? We’ve already seen lawsuits on such components like blog subscriber lists, Facebook and Twitter connections etc. This is going to become more blurred with the continued growth of social media. One way companies can keep the upper hand is to establish a fair and transparent process.
EXTERNAL INTEGRATION OF CUSTOMER DATA:
Companies have been bringing data together for many years internally, but they only know about what customers do with them. Now via external providers like Facebook or aggregators, there is going to be great interest in knowing about a customer holistically, not just the two-way relationship that companies already know. Privacy considerations included, these will start becoming available on the market.
BIG DATA:
Data trends we discussed last year continue to play out, but one megawave arching over all is Big Data. At the moment, this trend feels more like a solution looking for a problem at the company level. Although age-old techniques like statistical sampling are more cost-efficient, with the need to analyze data across, within, and outside companies and the larger market, more valuable applications will come to market and help realize the benefit of a Big Data strategy.
Predictive analytics needs a foundation of clean data. Here are top tips from our most recent lead gen implementation on Salesforce.com. You can use these immediately in any environment:
Address standardization and change of address. Typically up to 30% of records lack complete address information. This affects both deliverability and duplicate search. If you have not done so in 3 years, run a Change of Address (NCOA) process to get your customer’s new addresses. Stay up to date on your customers because 10% of businesses move each year. When you receive a street address change, but it’s a PO Box in the same city, retain both — one for mailing and one for secondary validation.
Dupes are the silent killer. Because a single comprehensive definition does not fit all, de-dupe the files using a variety of match logic. You can try a loose match logic (a few criteria, gives more duplicates) or tight match logic (more criteria, resulting in fewer duplicates). Take address elements into account, but use transactional information to determine which record to keep or drop. For example, between duplicates you might want to merge a record with multiple contacts into a record with the largest sales amount or the record with the longest time on file. Run dupes within your accounts, as well as against other sources. If you see the same record in two tables, delete or mark clearly why and set a shelf life to expire one.
Fix bad data entry practices. With key fields you use the most, particularly text fields, consolidate the misspellings and mixed-cases that make reporting difficult. As it can feel like a huge task, you do not have to do everything at once – fix just five fields this week, more next week. Here is an opportunity to update correction rules and fix legacy errors. As a bonus, segmentation and reporting become much easier.
Match to a B2B database or Tier 1 compiled list. While the obvious next step can be to append the firmographic elements for analytics, you can use information from the match rate to help further clean your data. Segment the unmatched customers and evaluate if they: are duplicated somewhere else, have failed address standardization, have any useful fields, or are possibly orphaned from a prior merge exercise. It’s springtime, prune the dead weight!
Append firmographic data. While technically it is not cleaning, append and enrichment of your data can provide big dividends from a hygiene perspective. Compare all contact information against the external data and fix format errors, missing extensions, suite number etc., while adding new contact info you did not have.
On a recent assignment for setting up lead generation, we took on an existing customer database to build a statistical model to score a leads database. The client does business in 7 countries, or so they said and I believed them. But they quickly added a caveat, “no one has looked at our database in awhile.”
First, we looked at their billing country field. This had been an open text field in their Salesforce.com system that could be edited by just about anyone. What we found was amazing. There were so many variations for each country that unique values quickly proliferated to 107. Misspelling, case difference, punctuation and abbreviations all conspired to create many versions of the same country!
Our first order of the day was to identify the obvious countries and group them, followed by corrections to the remaining data. This took 107 countries down to the correct 7. Then the country field was locked and going forward, a drop-down menu of countries is being used to prevent this proliferation from happening again.
For statistical modelers, why is cleaning data important for sales intelligence?
First of all, we want to point out that even a simple field can pose a challenge. In the absence of consistent coding guidance, it becomes hard to create a segmentation or master-filter at the top with which to analyze data. And here, it is not wise to mix multiple countries within the analysis.
Second, there may be other fields with which data can be cross-referenced. If there is a shipping country and a billing country, chances are that both ought to be the same. So correct the data, then carry over the values into the other field so you have fewer blank and invalid rows overall.
Third, the state of data quality can give a clue as to what other fields could be a problem. Even if not used in modeling, always keep in mind how this data was created, who and when it is updated, and what procedures are used to correct bad data.
Finally, proactively start addressing quality and instill an ongoing practice of making data cleanliness everyone’s responsibility. Have a method by which to collect feedback and incorporate it. This way, when you are ready to perform analyses, there are fewer surprises with respect to data quality.
Parting thought: Do not assume anything “should be obvious” or find where to lay blame for bad data. As you can see from the simple example above, all businesses have to approach data hygiene with care, caution and respect. As my friend’s father, an electrician said: “The day I stop fearing electricity is the day I will stop working.”
Think of the times in life when you are barraged with a tidal wave of information, but all you really want to know is, “what do I really need to know?” That inch-thick stack of mortgage paperwork. Credit card agreements. Mobile phone instruction booklets. Automobile owner manuals. Insurance policies.
Predictive analytics needs a foundation of clean data. Here are top tips from our most recent lead gen implementation on Salesforce.com. You can use these immediately in any environment:
