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.”
In this post we look at solving the critical business problem of managing the customer buying cycle, and how tools — simple to advanced — can help sales people support their customers.
In a repeat run rate business, customers are buying many categories of products and replenishing supplies and inventory on a weekly basis. With so many transactions from customer orders that reps must contend with, it is easy to forget about the customers who are NOT ordering, who are ordering less or not ordering the core high-value products any more.
While we are a predictive analytics company and have a solution to offer here, we also explore with customers and prospects who are starting out this question: What is the right fit for their needs? Here are three options in the order of features, scale and cost, with pros and cons we’ve discussed with them:
SALES PROCESS SOFTWARE
Primarily focuses on mechanically guiding rep actions through a rules engine: “call today … email next week … follow-up in 3 days.” Stand-alone or blends with CRM.
Pros: Most simple/easy to follow, ideal for a small team within a larger sales force.
Cons: Does not consider customer attributes and responses or historical sales data, and is not predictive.
MARKETING AUTOMATION SOFTWARE
Tracks frequency of contacts for communication, which is another way to stay on top of the buying cycle.
Pros: Maps to a pre-thought-out contact strategy, multi-channel, captures customer preferences and responses, and increasingly sophisticated. Ideal for 50-100 reps.
Cons: No historical sales data used, is not predictive, cannot blend external data, may not incorporate sales rep actions, does not identify cause and effect.
PREDICTIVE ANALYTICS SOFTWARE
This niche fits what we do. It is the most costly of the three because of data consolidation, involving expert statisticians with domain expertise, etc. But this process can scale indefinitely, it is predictive, and it can prove generation of incremental sales.
Pros: Support multiple sales channels, extensive data crunching, most accurate, provides cause/effect drivers. Ideal for 200+ reps, integrates with CRM seamlessly, best ROI.
Cons: Generally more expensive, more time to calibrate and start using, involves more people and technology (need expertise).
So there you have it. While we sell the hammers, aka predictive analytics software, we don’t see every problem as a nail that needs to be pounded. These tools all have a place in the sales arsenal, so take the time to determine what’s right for your organization.
For any airplane pilot, the auto-pilot is a valuable companion. It can automate routine tasks such as maintaining altitude and direction so you are free to focus on the next tasks needed to reach your goal destination.
Airplane auto-pilots can handle many tasks for the pilot. They can:
Follow programmed climb and descent rates to pre-set altitudes
Turn the plane to a specific direction
Line up for landing on the runway
Execute missed runway approach procedures, like climb away from the ground if the runway approach lights are not visible
But amazing as auto-pilots are, aviators are aware that the auto-pilot does not fly the plane for you. You are still always responsible for reaching the goal destination.
Business analytic tools – particularly those with predictive methods – can function a lot like an auto-pilot. They reduce workload, prioritize tasks and standardize best practices so you can focus on things the auto-pilot doesn’t do: continuously improving human performance, anticipating hazards far in advance, and staying ahead of the navigation tasks.
Like auto-pilots, how can analytics contribute to sales enablement and productivity?
REDUCE WORKLOAD:
Pilots joke that flying is a long span of boredom followed by moments of panic. Perhaps like all the sales activity during the final days of the quarter? In flying, reducing mundane workload is a top concern and this makes a big difference in the most crucial aspects of flight such as preparing for landing. Sales teams could offload mundane workload to auto-pilots as well.
Here are some ways analytics can reduce workload so you can perform where it counts:
Less time searching. Time spent searching is hard to document, but costly nonetheless. Smart Selling Tools suggests that only 218 days a year are “selling days” – that’s slightly more than six calendar months.
Align customer-brand preference. Manufacturers and brands run promotions based on their needs – clearing inventory, launchingnew products or gaining competitive share. These initiatives are often brought to the attention of sales reps in an ad-hoc informal way. But by using analytics to identify customer brand and product preference, price sensitivity and other customer attributes, reps can take advantage of promotions opportunities and contact targeted customers who are most likely to respond.
PRIORITIZE TASKS:
We all prioritize tasks, either by design or default. In the high-stakes world of aviation, prioritization brings a whole new level of professionalism, airmanship and eventually, delivery of consistently successful outcomes. Because successful outcomes must be achieved. Doesn’t that sound like a sales wish list?
Here’s how prioritization via predictive analytics can help your sales teams:
Prioritize based on predicted value. Sales reps must allocate time to customer conversation, learning, research and administrative work. Beyond applying good time management techniques, advanced analytics can further boost sales productivity. For example, there is significant value to developing a predicted value measure of customer interaction.
For example, predicted value could be the sum total of expected new orders, new product categories, and average order size of repeat orders. Based on this, the frequency and type of contact with the customer, level of effort/time, and type of offers could be varied to realize the value. Without this approach, sales reps are likely to focus on the trailing twelve month revenue which is a lagging rather than a leading indicator.
Suggest a “best course” workflow. It is not realistic to expect reps to know preferences across all customers and circumstances. But there are metrics that create a chain of sales activity – like a decision tree – that if optimally followed will result in a significantly higher customer value. How can reps achieve this?
Determining ideal “horizontal contact strategy” – in marketing parlance – is perfectly suited for predictive analytics. Rather than stop at one or two actions, this allows reps to see the relationship as a nurturing continuum. This workflow can then integrate with campaign management approaches so reps get additional support from marketing.
STANDARDIZE BEST PRACTICES:
No pilot will fly without a checklist. A checklist is neatly categorized with specific, sequential tasks to be done in a short amount of time, or a related set of maneuvers to do. For example, checklists include tasks for pre-flight, taxi, take-off, climbing and landing. The task instructions for each section are also specific to each type of aircraft, and they include manufacturer recommendations, learnings from experienced pilots, and recommendations from NTSB investigations – in other words, time-tested best practices.
Do checklists have a place in sales? Yes. You can use them to:
Test and Learn. Like marketing will test customer contact points — such as catalog page layouts and e-commerce offers – establish sales rep dimensions to test. These can be customer portfolio mix, product penetration, growth and customer loyalty. Test various combinations for sets of reps and determine the most profitable combination for customer, product and sales rep. These combinations become a checklist to follow.
Build analytics that recognize rep attributes. Some reps get growth from a small set of customers, others do well in certain product categories, others do well with a certain size book of business. Through predictive analytics, you can avoid painting reps with a single broad brush that may be counterproductive, and instead craft individual performance levels that are driven by how similar reps have performed historically.
To be effective, sales teams can benefit from centralized formation, the ability to derive insights, and the fortitude to simplify actions. This provides a sense of urgency that can best be leveraged through both predictive analytics, and the integration of these solutions into the daily stream of the rep’s work.
Just as safety is paramount in aviation in all aspects of flight, efficiency is critical to sales. Analytics can deliver that efficiency as a true companion in all aspects of selling.
Customer life stage is the standard bearer of segmentation. Customer stages like “new,” “active,” “lapsed” and “lost” serve an important purpose by grouping customers into homogenous, manageable clusters for marketing, value measurement and investment decisioning. However, these definitions have limitations that you should consider and correct prior to sales applications.
Here are key limitations and how to overcome them.
Measurement windows are too broad: Most of these segments have a 6- or 12-month horizon for comparison. A “new” customer stays in that segment for 6 months, often regardless of spend or activity, whether based on total spend or number of orders. Similarly, a “lost” customer is usually defined as any spend in the past 13–24 months, but zero spend over the past 12 months. As you can see both the time and revenue windows are very large, and that dilutes usefulness. Regarding time, this can be a few weeks or up to 24 months, and regarding revenue, the measure can be a few hundred dollars to several thousand.
Corrections: Create smaller segments for sales applications with sub-definitions. These can be arbitrary to follow a business threshold (i.e., 6 months or $2,000) or they can segment eligible customers into equal percentages, like 50-50 or 33-33-33 percent.
Another option is to create “run-rates” based on a larger window, but track smaller increments. For example, if “lost” customers are tracking 30% over 12 months, use a rolling 3-month window and 7.5% as the expected rate for this time window. Over time, you will arrive at a number that works for you.
Lack of interactivity: Once you have defined customer segments, customers are targeted for campaigns based on the static view. However, from a sales perspective, many factors influence customers’ migration from one segment to another. The quality of the rep (overall experience including with company, performance level), quality and quantity of the interaction, and tenure of the rep with the customer are all critical to influencing customer behavior.
Plus social media plays an increasing role in gauging customer sentiment. Social media gives many options for an alert sales force to listen and reach out proactively to customer needs.
Corrections: Allow for scoring within each segment based on interactions, and then have reps follow-up with customers. In the catalog world, it has long been known that product returns are actually a top predictor of repeat purchase. That hardly deserves a sales call, but for an early-stage customer this could be a significant differentiator to accelerate them into an active buying cycle.
Also by creating sub-segments, sales reps can initiate conversation with smaller changes in customer behavior, which leads to a more timely conversation.
Bias towards past or present value: All these segment definitions are based on what is already known about customers, not what is expected. With new customers, attempting to estimate their next 6- or 12-month value will be very helpful so you can take immediate actions rather than wait for customers to “prove themselves.” A placebo effect might create a self-fulfilling prophecy and grow value among new customers, but this is not likely to be the norm.
In the case of lapsed customers, the reverse is true. The emphasis is on the current value and less emphasis (or none!) on where the customers were 12-24 months ago. Thus customers who peaked much higher tend to get treated similar to customers who may not have such potential or share of wallet.
By looking at the past – or a single point – of value, trends also can be missed.
Corrections: Create future value measure for each outcome. Identify early stage customer trends to track and nurture new customers. When looking at retention, add “weights” for peak spend to give former high-value customers more priority.
Using two points of customer value allows you to capture trending info, and this is another way to differentiate among customers with an eye towards future value.
Set up a method you’re comfortable with that produces a future value. This can be simple up/down metric or a more elaborate statistical model. Within each life cycle segment, make a two- or three-way split of customers based on estimated future value. Have sales reps suggest and contribute additional metrics tied to their performance. For example, deeper category penetration as a goal with the early-stage customers.
In closing, for customer segmentation to bring better ROI for sales, three elements must be in place:
Actionability should be tactically focused with short windows
Have a future value orientation
Ensure that reps take responsibility for their customer interactions. Getting sale reps’ input, recognizing their workload/behaviors, and providing a long-term perspective can enhance the value of segmentation for the benefit of sales.
When you change your lead scoring and lead delivery using predictive analytics, don’t forget to train sales reps to think different as well.
It’s well-known that salespeople don’t qualify leads, they disqualify them. The more leads provided, the faster leads seem to get disqualified and bounced back in the holding queue. Reasons could be due to lack of data (such as invalid or no phone number), bias (“can’t possibly be a large enough deal”), or simply attitudes (“the more I close out, the sooner I will find something that works”).
This approach churns through leads, resulting in significant cost of acquisition and processing resources – both human and machine. If your reps’ closeout rate is 50%, your net cost is twice the initial cost! Marketers and sales leaders often respond by finding ways to deliver a greater number of qualified leads faster.
Predictive models are often used to score and deliver ideal prospects from a larger universe into outbound lead gen programs. Predictive models increase productivity and the ROI of achieving specific outcomes such as getting appointments or sales, moving newly-acquired customers into repeat customers, and improving cross and upsell.
But caution: predictive models may produce leads with characteristics that are counter-intuitive.
This is where you get hot leads to handle with care. By their nature, scored leads will be a lot fewer — typically 20-40% of available leads. High-scoring leads may have attributes considered not desirable, like the number of employees. These leads may also not have some fields that reps typically look for, like contact title. Yet these leads have been predicted to produce a high level of performance.
This leads to a few lessons:
Teach reps to look past some traditional criteria and look at the prospect holistically.
If reps continue their old practice, they may close out leads more likely to convert. And because these leads are fewer in number, upon exhausting them reps go on to scour other lead sources that perform significantly worse, as we’ve found upon back-testing. This socks a double whammy to your performance and productivity. For maximum performance, do not allow reps to pull leads from other sources unless all the scored leads have been acted upon.
Leads that score highly for one outcome, such as getting an appointment, are also more likely to produce a sale, at a higher average order amount, and with more add-ons. Train reps to nurture these leads to achieve such multiplicative sales value.
In short, predictive analytics leads you on a fundamental shift from tactical to strategic thinking. Predictive analytics may deliver fewer but better leads – be careful to not burn through them. Instead, profit from their sales potential.
This post was inspired by a recent entry on The Sales Blog that discussed how it is easy to do the enjoyable things, when instead we should focus on things needed to improve sales results. Such as, resolving customer problems. Problems with a customer don’t go away, unless the customer goes away.
So from our quantitative perspective, how would we suggest making it easier for salespeople to engage in difficult conversations? Three ways this could be done:
Mix up the servings. Segment your customer portfolio based on buying cycle. Ensure that the salesperson is calling on all segments in some proportion that reflects both gains in immediate sales and long-term relationship. Use the CRM system to set up call blocks that are driven by analytics, to ensure consistent actions across the entire sales force.
Demonstrate the loss from not making the call. Salespeople hate leaving money on the table. To show how much could be left behind, define a customer segment to call first as suggested above. Then identify actual sales revenue from sales calls made previously to a similar customer segment. Use this revenue figure to establish a per-customer baseline of incremental sales generated. Multiply this figure by number of customers in your “to call first” segment. Use this to show sales reps how much revenue may be lost by not calling. You can also extend this lost revenue estimate into a longer time horizon, showing revenue lost from what would have been future repeat purchases.
Remove the responsibility. Give the salesperson a time limit or maximum number of attempts to make the call to a priority customer identified by the analytics. For example, consider a program that reaches out to lapsed customers. This is often a difficult conversation – the rep knows the customer was a great customer at one time, the rep did not keep track and call proactively, and the customer moved to a different supplier. By removing this account from the rep’s portfolio, the account can be put into a nurturing program, given to newer reps who are more hungry, or other approaches for a fresh start.
The Sales Blog article speaks about salespeople enjoying taking customers out to ballgames. So we’ll leave our readers with this thought: when treated as a defensive strategy to keep a failing account, we have found that this socializing approach actually backfires. After the event, most customers subsequently curtail further or reduce spend to zero with the company. We were very puzzled – could this fun outing actually be a catalyst to losing the account? Because most likely, the difficult conversation to square with the customer upfront did not happen.
Just like the old proverb, “a stitch in time saves nine,” there is a right time for these necessary conversations. Predictive analytics can help detect patterns that identify when that first stitch is required before the relationship is torn beyond salvage.
Inside sales teams are at it every day, making thousands of calls to prospects, seeking an appointment or a sale. They are given scrubbed lists with contact names, job title, phone numbers and a good luck pat. On the back end, managers track calls, appointments and sales. The cycle continues when reps deplete their assigned leads and it starts over again.
There are times when this well-oiled prospecting machine can under-deliver – and you may not be aware. But there are easy fixes. Here we explore three cases, and discuss how to overcome these challenges.
TESTING NEW MARKETS: When looking at sales data, you may find strong traction among companies that don’t fit the best customer profile — or at least what the rep thinks is the best profile. This finding is usually uncovered by in-depth profiling, micro-segmentation or modeling analysis. These customers may not be among the largest customers, but you find that they purchased numerous units of products that fit specific needs.
As an example, say you are selling technology products, and religious institutions are not known as leading technology buyers. But recently we came across a church buying hundreds of iPads for one of its programs. This is opportunistic entry into a market if other religious institutions have similar programs. What you can do:
To aid sales calls, share historical product purchase data with reps so they lead with iPads and related iPad cross-sell products instead of laptops, printers or software.
Show reps the sales potential of these calls when they may understandably question this. Pull examples to show similar sales to this market and the value of the deals.
To help target this new vertical, it is important to match the highest product propensity to the leads in this vertical.
MISSING INFORMATION: Even when providing a scored list through predictive modeling, some information valued by sales reps for their calls may be missing.
In one case, sales reps for a technology provider perceived the presence of a website as a surrogate for computer purchases. In our B2B database of 14 million businesses, this field is only available for 22-25% of sales prospect records. In our scored file this info was available for 35% of records – certainly an improvement, but reps were concerned about the two-thirds of leads without this info. The scoring rated these leads as strong prospects but reps were uncertain about calling them. What do you do?
Have them do a quick Google search on company name and state. The company may have reviews, social media presence, etc. that would otherwise confirm they’re “tech-savvy,” and if there’s a website it will likely turn up.
Show cases where leads without this information have progressed through the pipeline and converted successfully.
Explain that the process of data collection, while well-defined, is not always perfect. Set up specific steps they can follow to cross-corroborate other fields and deduce the missing information.
PROSPECTS HEAVILY SKEWED: Recommendations from predictive modeling may favor market segments that reps do not normally associate with large sales. But the model shows easy smaller sales opportunities exist.
One of our projects produced recommendations where the prospect company size varied from 100 employees to as low as 8. Most records were towards the lower end, reflecting the business universe and perhaps implying employee size was not an important predictor of sales. These leads were produced by the scoring algorithm and further fine-tuned as most likely to respond. However, reps were suspicious of the potential of prospects with fewer employees. When reps are not completely trusting the sales potential:
Provide them with evidence that shows why these prospects scored high. Share the desirable attributes of these prospects.
As one option, using Industry (SIC) distribution, create “blocks” of leads that contain a mix of company sizes and provide guidance that x% are expected to convert for each of these “blocks.”
A benefit of predictive analytics is the ability to score based on a large number of attributes (500+ variables is not uncommon for our models), reaching beyond the boundaries of even human intuition which no doubt can bring profitable insight. We don’t discount that intuition; rather, predictive analytics should lead you to new sales from new markets. The flip side is that these new markets and new customers may not intuitively look like great sales opportunities at first glance, precisely because they’re new and different.
But acting on model recommendations is needed to break through to faster growth and higher sales. This is where sales managers can mentor reps to see the opportunities ahead. Help sales reps trust the idea of calling upon these new opportunities, and the potential conversion rates and quota attainment, even when reps may have previously rejected some of these opportunities. To get the true gains possible with predictive analytics, you need to build the trust and confidence to push beyond a business-as-usual routine.
If yours is like most sales organizations, there is at least one program or motivational initiative with a fun theme to it, possibly involving sports or other games. A desire for recognition and winning brings out something extra in us, and what better place than sales to showcase that, where there’s motivation to scale new heights against the competition and to personally and professionally benefit.
According to gamification.org – a terrific collection of resources – gamification is “the process of adding Game Mechanics and Rewards in non-game contexts to boost Engagement, Loyalty and Fun!” If that doesn’t seem intuitive, you’ll recognize it from first-hand experience because a popular application of gamification is frequent flyer programs. Gamification is simply a methodical approach to engage your audience, create a two-way give-and-take, and connect with people’s motivations and aspirations.
In a business context, engagement through games can be targeted towards customers, sales reps, partners and even employees. Let’s break gamification strategy down to its core components:
Game mechanics are the building blocks of the process. Gamification.org says these “are constructs of Rules and Feedback Loops intended to produce enjoyable Gameplay that can be applied and combined in any context.” According to gamification.org, there are 24 distinct blocks to assemble. Here we will focus on a few most important for sales enablement and productivity.
In sales as in life, you have two kinds of people:
Those who are self-motivated (intrinsic).
Those who need an outside factor to motivate them (extrinsic).
To appeal to these types, from the 24 items described by gamification.org, we have created logical groupings called intrinsic (what matters to the individual) and extrinsic (about an aspect of the game or community) to organize the building blocks of game mechanics. We’re not claiming to have the final word on these groupings, but rather invite your input on them.
Intrinsic
Extrinsic
Achievements
Appointments
Behavioral Momentum
Bonuses
Blissful Productivity
Cascading Information Theory
Discovery
Combos
Epic Meaning
Community Collaboration
Free Lunch
Countdown
Loss Aversion
Infinite Gameplay
Ownership
Levels
Points
Lottery
Quests
Progression
Urgent Optimism
Reward Schedules
Status
Virality
When you combine the right mix for extrinsic or intrinsic motivation personalities, you can leverage these to achieve superior results from sales reps and sales organizations. Take stock within your group of who is likely to belong to one group or the other, and apply the right levers.
Think about these game building blocks and consider their application to sales, and where you could implement them. For example, they could be part of a compensation structure, employee retention plan or customer service scores directed at the inside sales rep team. As you can see, gamification can have many avatars!
We shall stop here in this article, and continue with the next component — Game Design — in the next post.
This article first published on Focus. This is the first post in a multi-part series about: (a) the concept of gamification, (b) its business uses and applications, (c) trends and forces accelerating its importance, (d) quantitative analysis considerations, and (e) how it can used to drive sales enablement and productivity. This post introduces the concept of gamification.
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.”
For any airplane pilot, the auto-pilot is a valuable companion. It can automate routine tasks such as maintaining altitude and direction so you are free to focus on the next tasks needed to reach your goal destination.
Measurement windows are too broad: Most of these segments have a 6- or 12-month horizon for comparison. A “new” customer stays in that segment for 6 months, often regardless of spend or activity, whether based on total spend or number of orders. Similarly, a “lost” customer is usually defined as any spend in the past 13–24 months, but zero spend over the past 12 months. As you can see both the time and revenue windows are very large, and that dilutes usefulness. Regarding time, this can be a few weeks or up to 24 months, and regarding revenue, the measure can be a few hundred dollars to several thousand.
Lack of interactivity: Once you have defined customer segments, customers are targeted for campaigns based on the static view. However, from a sales perspective, many factors influence customers’ migration from one segment to another. The quality of the rep (overall experience including with company, performance level), quality and quantity of the interaction, and tenure of the rep with the customer are all critical to influencing customer behavior.
Bias towards past or present value: All these segment definitions are based on what is already known about customers, not what is expected. With new customers, attempting to estimate their next 6- or 12-month value will be very helpful so you can take immediate actions rather than wait for customers to “prove themselves.” A placebo effect might create a self-fulfilling prophecy and grow value among new customers, but this is not likely to be the norm.
When you change your lead scoring and lead delivery using predictive analytics, don’t forget to train sales reps to think different as well.
This post was inspired by a recent entry on 
Inside sales teams are at it every day, making thousands of calls to prospects, seeking an appointment or a sale. They are given scrubbed lists with contact names, job title, phone numbers and a good luck pat. On the back end, managers track calls, appointments and sales. The cycle continues when reps deplete their assigned leads and it starts over again.
People like fun n’ games.
