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Big New Costs for Salesforce Apps Using Google Maps

Valgen cfMAPP Google Maps App for Salesforce CRMHave you been using the Salesforce app called “Find Nearby Accounts, Contacts, Leads” from Salesforce Labs? It’s been a free app. But as of February 1, 2014 it will not really be free. You will need a Google Maps API for Business license to continue using the app. And that license cost starts in the tens of thousands. Yes! You read that right. (Updated April 6, 2014: The “Find Nearby Accounts” app is no longer available.)

So what if you don’t want to pay tens of thousands of dollars? What can you do? Well, our cfMAPP app is a great alternative, if we can say so ourselves. But don’t take our word for it – see what cfMAPP users have said about it, below. cfMAPP does what the “Find Nearby” app does, and much more. You can find complete info about the app here.

Why Did This Change Happen?

This change is due to an update to Google’s term of service for its Maps API. Yesterday, Salesforce sent an email notice to system administrators, notifying them of the change to the “Find Nearby – Accounts, Contacts, Leads” app.

If you are using another AppExchange app that uses Google Maps, you should ask whether the app provider has a commercial agreement to use Google Maps. If they do not have a commercial agreement, the app may stop working, and/or you may need to get your own Google Maps license annually to continue legally accessing Google Maps through the app.

We saw this coming months ago. Rather than use Google Maps in our cfMAPP product and pass a large additional cost on to our customers, we’ve found another map solution for cfMAPP which allows us to maintain our pricing level for you.

What Do Users of cfMAPP Say?

Hundreds of sales reps are using cfMAPP during their sales calls. They show Nearby Accounts to prospects on maps during live online demos. They use the maps to find nearby customers and do a little name-dropping. While most Salesforce map apps are designed for field reps who are driving to appointments, cfMAPP has many features that meet the needs of inside sales reps. Here’s what some of them say:

  • Brad, a marketing director who has been a client of Valgen for many years, told us: “This has made it easy to speak to a prospect as though we are local. We drop customer names that they recognize and resonate with, building instant credibility.”
  • Nick, a training manager for a closing team said: “Our closers show this to the prospect on every demo. We can’t be without it.”

Visit our website to see more about the app including screen shots, and how to contact us about it.

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cfMAPP: Recommended Sales Productivity Tool for Inside Sales

Today, Valgen’s cfMAPP is the featured recommended tool by Smart Selling Tools. The video below explains how this “map app” for Salesforce helps inside sales reps get better success with appointment-setting during cold calls:

As Nancy Nardin of Smart Selling Tools says in the video, “no one likes to be treated like a name on a cold call list.” What can you do about that? Well, when inside appointment-setters can mention nearby customers, it’s clear to the prospect that the sales rep did some research. Maps are a natural, visual way to do that research productively.

Sales reps use cfMAPP to get pop-up maps in Salesforce, similar to Google Maps. Customers and prospects are automatically plotted. The sales rep just clicks a button to get this. Depending on how many customers are in the area, the map might look like this:

Valgen cfMAPP Google Maps App for Salesforce CRM

Customers are listed in Nearby Accounts, and the sales rep can mention who you currently do business with.

It makes cold calling more productive because reps can see customers on a map, scan the information easily during a sales call, and share company names that the prospect might be familiar with. Couldn’t that make appointment-setting conversations while prospecting so much more successful?


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Google Maps in Salesforce CRM: How it helps inside sales reps set appointments and win sales

Inside sales reps using our cfMAPP app for Salesforce have found it helps close the miles between them and prospects. It helps build trust and rapport. The reps may be calling from far away, but cfMAPP lets them see nearby accounts in the prospect’s neighborhood, through Google Maps integrated with Salesforce.

It allows reps to name drop, and that local knowledge builds the rep’s confidence during the conversation. Reps can even show nearby customers to the prospect during online demos.

We’ve had people ask, so how does this play out? How does it work? It’s obvious how Google Maps in Salesforce can help field sales when they need to plan efficient driving routes. But how would it help inside sales? Well, here’s a story of a sales call that shows how cfMAPP has been successfully used by inside sales reps during calls:

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Why predictive models are better than a simple average

Why-Be-AverageDuring a recent sales call, we were asked if instead of our predictive modeling app could they use “average days to call” as a metric?  They were referring to our cfTIME app for Salesforce, which predicts time to next purchase and then determines the right time to contact the customer to increase the probability of a sale.

So, you could look at two definitions for average (didn’t you know, in statistics there are two of everything, that’s how we cover our bases!):

  1. Average buying cycle (ABC). This can be defined as 365 days divided by number of orders. If there are 8 orders in a year, then 365/8 = 47 days. Note that you can also choose a calendar year or trailing twelve months.
  2. Time to Next Order (TNO).  This is the expected days to next order. One approach is to average out the previous few orders. So let’s say the last three orders were 25, 10 and 45 days apart. The average of this is (25+10+52)/3 = 29 days.

Now, the answer to this question has two parts:

  • Technical feasibility
  • Build vs. buy investment

Technically, our models predict at an accuracy of 80%. That means at any given point in time, 80% of the customers will buy within the window we predict for them.

But if you used an average, assuming a normal distribution, here is what you will find:


As you can see, only a small percentage of customers fit within an optimum time window for a sales contact. Customers whose buying cycle falls short of this window are often your most valuable customers. And customers who are above this average are less valuable customers who will take up a disproportionate share of your resources and will likely not yield a positive return.

Next is the question of ROI for an app such as cfTIME. What will it cost to build vs. buy? When you buy an application, the app price is a transparent cost. However when you build, there are a lot of hidden costs that cannot truly be accounted for. Employee time, hardware and software costs, efficiency (or lack thereof) are some of the costs you have to take into consideration. Further, there is an opportunity cost when your marketing vice president is spending a lot of time on something that is less effective at moving the needle in terms of sales.

A predictive model is, quite simply, an outcome-based exercise. We are either predicting more sales, more orders, higher average order sale or more categories. If a model does not deliver against those outcomes – which can be measured by applying rigorous methods – it can be modified to improve the outcomes. A simple “average” cannot deliver this.

We definitely support using simple metrics in many cases. Averages are directionally good, but if misused operationally they can have devastating consequences – ones that you may or may not be able to measure until it is too late.

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Sales Managers: Are you a geek at heart? Do you like to drive results through numbers?

Sales Success with NumbersOver the course of our careers, we’ve had the privilege of working with dozens of sales managers. In many ways, managers hold the key to the success of their sales reps. They know each and every rep well, understand the customer and prospect mix each rep has, the market that reps are operating in, the relative difficulty of making the quota, etc. They capture and impart this knowledge during their one-on-one discussions with reps.

Such tribal knowledge is often not institutionalized throughout the sales organization. The result is lack of repeatability, consistency and scalability of results – which means they are starting over with each new rep, team and division that they move into management.

Certainly factors like compensation, incentives, coaching and product training play a role in this success too. But we see distinct traits in the sales managers that are attracted to work with us – they use numbers, statistics, data and models to get the best performance from their sales teams. Here are a few traits that we’ve seen, some challenges that they’ve faced and why they’ve chosen to work with us:

TRAIT: They desire to “do something different this time.”

As ideas like call blocks, teaming and Hawaii trips get suggested, they realize these are maybe one-off, not trackable or simply not proven to bring enough of the performance they need.

CHALLENGE: They are looking for a scalable solution they can point to every rep and say “do this, it works, did you do this?”

REASON: Predictive analytics provides that scalable, stable and tangible call to action that every rep can do. By using lead generation solutions liked scored, quality leads, managers can ensure increased sales productivity for every rep.


TRAIT: They play with large spreadsheets – a lot.

These managers often have downloaded the entire customer data from Salesforce, pivoted it, summarized it and they’ve looked for patterns to share with the reps.

CHALLENGE: They know they are on to something, but this is on top of everything they have to do. And although they have great Excel skills, they are running out of time … or memory on their laptops!

REASON: Predictive analytics gives them all the information they need on their fingertips. Instead of trying to do the mathematics and analysis themselves, they can now see the patterns forming and be left to do what they do best: manage and coach the reps.


TRAIT: They constantly seek new data.

Sales managers with a quantitative leaning know that data they have may not be enough to make the sales cycle quicker and certain.

CHALLENGE: They are buying leads from various sources, appending demographic data, mixing in product usage data and contact summaries to create a broader picture. They tend to look at social media data too. With so many sources to keep track of, analysis of their sources and quality control can become quite problematic.

REASON: Predictive analytics can give them the broader, larger picture, compiling the data, trends and statistics from all sources.


TRAIT: They focus on “the middle 60%.”

They leave their top 20% of sales reps alone, and they know who their bottom 20% are, but they want to improve results for everyone else. They look to analytical methods for improvement.

CHALLENGE: They know there is a cyclical effect that helps the entire organization, and these middle 60% are the difference between making the numbers this month vs. making numbers consistently. They believe in incremental change. The sales managers we work with want to improve x% of the reps, get x% more customers this month, increase average order by x% and penetrate x% more categories. They want consistent gains across many dimensions that are both scalable and sustainable.

REASON: Using predictive analytics tools to score leads, to gauge customer timing and to stay on top of buyer cycles can reduce variability and give the middle tier reps a big productivity boost, thus creating consistency across all reps, almost like making “the rest like the best.”


Incidentally many of these sales managers are also in growing companies, and they’re hungry for even faster growth. They act as a buffer between understanding the math and delivering a simple message to the reps.

Most of all, they are very inquisitive and inherently believe that an analytical approach does work. They have to believe this, in order to give us even 10 minutes of their precious selling time. That makes all the difference.

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