Startup Salesforce Tip #1: The Founder Welcome Email

This is the first in an occasional series of my favorite sales and marketing tips for startups and SMBs. Enjoy! –David

One easy and effective marketing best practice is to send out a personalized welcome email about a week after customers sign up for your application or service. Ideally this should be a personal note from one of the founders, short with no sales pitch — more of a “checking in / introducing myself” email. And Salesforce makes it easy to do .

The idea is for this email to be much less formal than the typical welcome emails / autoresponders that go out right after signup, and which contain lots of information and links. It should be signed by one of the founders or an executive whenever possible (a Sales or Marketing title will decrease response, depending on the type of customer base), and should contain as much personal contact information as you feel comfortable giving out.


Hello {!Contact.FirstName}

I just wanted to follow up with you personally, and make sure you were having a good experience using [Product].

Please let me know if I can provide you with a demo, connect you with anyone on our support team, or help in any way. My contact details are below.


John Smith

This may seem silly or to be a waste of time, but there are a variety of reasons why it is worthwhile.

  • Additional Touchpoint. Some people will realize this is a form email, but it seems less automated than the system emails people get when they signup. This perception will vary quite a bit depending on your target customers, of course (technical people are very different from marketers, B2B very different than B2C). But in general it is a good excuse to reach out to customers which few will object to, and you may get more responses than you would think (it is easy for someone to dash off a quick response to a seemingly personal email).
  • Identify serious customers. Many customers will appreciate getting the personal contact information of a founder or principal, or really anyone at the company, especially in B2B products where having an actual person (or a person beyond a sales rep) could be beneficial to them as they become a serious customer. They may respond to the email, allowing you to further identify them as qualified.
  • Identify unqualified customers. Sometimes people will respond with “your product sucks” or “take me off your list.” Never discount the value of a NO, it helps you get to those who will buy that much faster. Just make sure you update their customer records as unqualified / email opt-out in Salesforce.
  • Identify misconceptions. A few customers will respond with a variation of “I like your product, but I am not going to use it because it does not do X.” When in fact your product does do X, or maybe there is a workaround, or maybe you are coming out with that feature next week. Good to know.
  • Gather additional data. Not everyone will like this one, but when people reply often additional information is available in their email signature, or their gmail address forwarded to a corporate email address that they reply from. Since so many signup processes for web apps these days are trying to be ultra-low friction, requiring just email address and password or less, I like having someone enter this information into Salesforce whenever you can.

When and How to Send

I recommend sending these emails once a week to everyone who signed up in the past week, using the Salesforce “Mass Email” feature (available in Professional Edition or above). After some initial setup, it is a fairly quick process to do it every week.

Salesforce makes it easy to send out the emails, you don’t have to pull lists into an external system, and best of all you can also remove recipients who are already corresponding with a sales rep or other person at your company (filter out based on last activity or visual inspection).

First, create an email template similar to the one above, or create your own to match the positioning and voice of your business. In Salesforce, go to

Setup > Email > My Templates > New Template

Create a new template, add any merge fields you want (I prefer first name with a Hello greeting and no punctuation, so if there is no value in the customer record it doesn’t seem awkward like “Dear”). Be sure to check the box that makes the template available for use.

Second, go to the Contacts tab and scroll down to

Tools > Mass Email

at the bottom right (Assuming you are importing all signups as Accounts, otherwise go to the Leads tab).

You have to create custom views of recipients for a mass email, but each week you can then click “edit”, update the dates, and “Save as” to get a view for the new batch. In the example below, I am selecting accounts that were created in a single week, and then using the filter logic to also select accounts that either have no activity (no calls/emails recorded on the account), or at least that haven’t had activities in the last day (“last activity less than yesterday” in Salesforce-speak).

You should adjust those values to match your follow up and how often you want to contact customers, and you can also add in additional filter criteria (such as Account Type or custom fields that segment your customers such as Plan or product type).

Once you have saved as, you will see a list of customer emails. If you would like, you can manually scroll through them and uncheck any contacts that you don’t want to receive the email. Click next and select your email template, then next and select your settings for when to send the email. I recommend selecting “Store an activity for each message” so anyone looking at the contact later will see that they received the email. You can also choose whether to “Use My Signature” that is in the email setup, or include a custom signature as part of the template.

Click send, and wait for the response.

Then set a calendar reminder, and in one week change the date fields on the view, save as, and you’ll be ready to go all over again.

Caveat: If you are getting more than 500 customers a week, you will need to do this more frequently (Salesforce limits mass email to 500 per day). A lot more than 500 and you probably can’t use Salesforce efficiently for this, so use whatever external system you use for newsletters and other large email volume (Mailgun, SendGrid, MailChimp, ExactTarget, etc.)

But I already get too much email

I’ll leave you with a final tip I’ve seen some startups employ to deal with this, and it is a clever hack that should work for awhile. If you are worried as a founder about getting more email in an inbox already filled with critical emails from customers, partners, and investors, you can set up an alternate but plausible email address. For example, if is your primary address, you can set up as an alias that goes to a separate box, or even an alias that goes to you and a Sales person who is tasked with responding to customer inquiries.

Then filter email to so it doesn’t go into your inbox, and only reply personally when you need to. You are just lending your name and credibility to the email — if you can follow up to responses personally that is great, but if not, it generally is not a big deal as long as someone responds.


CloudAmp provides Customer Analytics for SaaS companies, directly inside of Salesforce CRM. If you have a trial or free plan, we can show you who your best leads are based on their real-time usage.

New CloudAmp DirectEngage Presentation

Check out our DirectEngage product presentation, now slightly less boring with music and animations thanks to SlideRocket.

Do you know… Which Free / Trial Users are your best leads?

What if you could see… Your App’s User and Usage Data, right in Salesforce?

>> CloudAmp DirectEngage Presentation 

(opens in new window, Flash required)


CloudAmp provides Customer Analytics for SaaS companies, directly inside of Salesforce CRM. If you have a trial or free plan, we can show you who your best leads are based on their real-time usage.

Free Trial Predictive Modeling in 5 Steps (No Statistics Degree Needed)

Most SaaS products have a free trial or freemium plan. If you are serving trial customers, whether in Sales, Marketing, or Customer Support, you need to be able to predict which customers will convert to paid, so you can optimize your customer interactions based on that information.
With all the talk of big data and data scientists these days, using a term like “Predictive Modeling” may conjure up scary images – teams of engineers with giant Hadoop clusters, or indecipherable equations covering a whiteboard.
But there are easy ways to predict which customers will convert and which will not, using data that you are probably already collecting.



Step 1: What do the usage patterns of your Best Customers look like?


Examine your best customers, and how they are using your product. What usage metrics do they have in common? And when they first sign up, is there a common pattern to how they begin using your product?


Every business will have different metrics that define the customers who are most engaged and most likely to convert, and even within the same company you may have differences between customer segments. Depending on the specifics of your product, we want to look at usage metrics like the following:

  • number of API calls
  • amount of data stored,
  • number of servers deployed
  • number of actions taken
  • number of records created
  • number of users per account (or linked accounts)
  • etc.

There are some metrics common to most web applications, which can also be useful metrics to look at:

  • Number of logins
  • Last login
  • Signup date
  • How soon they started using the product
  • Time period they used the product consistently
  • etc.

You may only have a partial idea of what your best customers look like, or be unsure where to set the thresholds for segmenting your customers. That is ok, as you can always adjust the model over time as you learn more, or compare the effectiveness different models. Pay particular attention to how your best customers and most enthusiastic fans use your product, especially when they first sign up.


Using all of the information above, define one to three metrics that correlate to your best customers, and determine some thresholds that will categorize how engaged or likely to convert each customer is. For example, customers who have signed up in the last 60 days and created over 20 records, or customers with more than 5 servers who installed a security agent on more than 2 of them.



Step 2: What about your (almost) Never Customers?


The second step is more important if you have a freemium (free forever) pricing tier, but should be well understood for free trials as well. Are there groups of customers who almost never convert, or are generally unqualified to buy your product in some way?


For example, if you sell a product which can be used by both businesses and prosumers / hobbyists, there may be a segment of your customers who will never upgrade to paid, as they simply don’t have the business need or cash flow to justify paying.


This is not to say that these customers never have value. If you are selling to developers or providing open source solutions, having large numbers of unpaid customers can be critical to fostering a user ecosystem. For many B2B products, free users can serve as a long term lead base if they switch jobs and are in a position to use the product at their new company.

But given that your resources are generally scarce, you want to focus your sales and support efforts on those likely to convert. Knowing who is likely to be “unqualified” is nothing personal, just an important way to segment customers and prioritize interactions.


Step 3: Any Getting Started Trends / Warning Signs?


Step 3 may be less important than the previous two, depending on your business, but it pays to think about usage as it relates to time, to complete our predictive model. Are there actions that people take when they first sign up that are an indicator that they are likely to convert?

For example, they complete their profile or create a new project, enter an API key or connect via OAUTH to a 3rd party system, or perform X actions in less than Y days. Those actively looking for a solution that your product solves will exhibit very different initial actions than someone who just saw your company on TechCrunch (and who signs up for everything they see featured there for some reason, regardless of what it is).


And what about the duration of their usage? For example, one recent SaaS industry survey found that “Free trial users who were still active during day three of their trial were four times more likely to convert into paying users than the average customer.”


Perhaps this seems like an obvious conclusion, given that in many free trials 50%+ of signups don’t even bother using the app at all. And another large chunk of users play around for a few minutes and then get distracted, never to return. But if the three day statistic is true for your business, you would want to follow up with those customers more aggressively, or at least send different Emails to those users right away instead of more generic marketing emails later on.


Conversely, are there patterns of usage and then decline which are warning signs? Perhaps customers start using your app and then run into problems. They burn through thousands of Emails or API calls and their usage then trails off, or stops suddenly.


If you can proactively identify those customers and contact them, there are some real sales opportunities there (often they are having a simple problem, or may have a misunderstanding of your product’s capabilities). This data can also be useful for churn prevention in the future for a customer success or support team.


Step 4: Connect your data to your CRM system


The next step is to connect your user and usage data from your SaaS / Web App backend database into your CRM system. By integrating your app’s data into your CRM, you gain several advantages.

First, you empower your sales and support people (assuming they are using the CRM) to see customer usage data right where they look at other customer information. Having to log into another system, or viewing customer usage in an administrative web page or Excel sheet that is outside of their regular workflow is inefficient at best, and will result in them not using the data at all in many cases.


Secondly, you can use all of the structure and functionality already present in most CRM systems to manipulate the data and calculate the predictive models we have set up in steps 1-3. Otherwise, you have to instrument all of step 5 in your app, which is generally not something that Engineering is going to want to prioritize over features in your product.


Once all of the data is in your CRM system, a good marketing analyst or sales operations person should be able to customize views, set alerts and triggers, build reports, and more. This both empowers your team to work with their data without engineering help, and makes things more nimble as your company gets experience working with the predictive model and updating the model and associated analytics as it evolves.


Step 5: Implement the Predictive Model in your CRM


Finally, the fun part. Once your customer usage data is being updated into your CRM system, you can implement the predictive model based on steps 1-3 above. First, we start with the raw usage data that has been imported, and typically create summary or roll up fields which total up those records, giving you some metrics (Total API calls, Total Builds, etc.)


Depending on the complexity of your model, those totals may be enough (a predictive model could be a simple as Total API calls > 1000). But often we want to consider several metrics to come up with a “lead score” for your predictive model (for example, Customers who have > 5 GB stored currently but have had >100 GB stored over all time could generate a “storage elasticity” score for those customers, where that metric might be important in identifying a certain type of customer who is likely to convert — or for a different product, likely to churn).


A score can provide a more nuanced model, as well as a way to sort or prioritize customer accounts in the CRM system. But whether you build the model from a single metric, or a number of metrics pulled together by a formula, you can then use the model to begin organizing your customers in the CRM system.


You can modify the views that certain sales reps see, so unqualified customers don’t show up in their main screen, reducing the signal to noise ratio and making sales more efficient. Views can be sorted by the new predictive model lead score, so the hottest accounts bubble to the top automatically as their usage changes. For certain scores you can use workflow rules or triggers built into the better CRM systems to even assign follow up tasks or send email alerts to your team members.


Next, aggregating all the usage metrics and predictive scores into reports provides analytics and dashboard visibility for your managers and executives. With customer usage data being updated in your CRM, right where the new accounts are created, you get to see trends and understand changes in customer usage, signups, churn and more, faster and easier than would generally be possible with manual Excel reports. And business intelligence is now accessible to a larger number of people, some of whom can be empowered to create their own reports to derive further value from the predictive model scores and usage data.


If you have read this far, think to yourself what visibility you have into trial customers, and how they are using your product. Chances are you can create a predictive model to understand where customers are in your funnel, and improve conversions and revenue, in just a few easy steps.



CloudAmp provides Customer Analytics for SaaS companies, directly inside of Salesforce CRM. If you have a trial or free plan, we can show you who your best leads are based on their real-time usage. Customer Case Study provides a cross-platform app framework that is the simplest way to build native iOS, Android and web apps using a single HTML5 codebase. The company’s main product, Forge, consists of a JavaScript API that exposes native functionality such as camera access and notifications, along with a set of tools to build your app for each platform that you want to support.


When was preparing to launch Forge, they wanted to have better tools for managing their expanded sales and marketing efforts. And with many developers using their free “Get Started” plan, they wanted to follow up with any customers who were actively using the product.  


“We needed to easily see who was using our product, and how they were using it,” said Amir Nathoo, Co-Founder & CEO. “Ours is a fairly new category, so by engaging with our active customers, we can often help them see how the different parts of our product can have a big impact on their apps.”


CloudAmp started by implementing Salesforce for, and migrating data from their existing Gmail-based CRM. We then set up our DirectEngageTM customer analytics product, which extracts data from’s Amazon RDS backend, pushes it securely via https to CloudAmp’s servers, and then maps it into Salesforce in real-time. can now see how each customer is using their product, in real-time on an account-specific level in Salesforce, and in aggregate to understand trends and opportunities. Each major user action in the Forge software (Package, Generate, Run, Build) is immediately pushed into Salesforce as a new record, and fields in the account show totals for automatically scoring the accounts, and reporting customer activity.


“Before we had an internal web page where we could see a list of active customers,” said Sahil Jain, Co-Founder & CMO. “But with CloudAmp, now we have each customer’s full contact information in our CRM, along with exactly how they are using our software. We can be much more efficient in connecting with customers for sales or customer service.”


To complete the project, CloudAmp built a series of reports and dashboards for, to provide business intelligence that anyone in the company can easily access. With the data integration running continuously in real-time, these reports automatically are updated with the latest data on signup trends, active customers, and other key metrics.


As the sales team explores the functionality of Salesforce, we are assisting with workflow rules to set follow up tasks, and the new data will make it possible to build models of the top customer’s usage patterns. Those patterns can then be used for segmentation reports and predictive models to proactively identify new signups with a high likelihood of becoming high value customers.




CloudAmp helps software companies centralize  customer engagement, usage and account data in their CRM.

Increase your conversion rates, customer retention, and revenue by gaining real-time visibility into how customers are using your App, directly within Salesforce.


The Curse of Utility Pricing

Internet services are increasingly being priced based on usage. Whether you call it utility-based billing, on demand pricing, or pay-as-you-go, many applications and services in the Cloud are priced this way.

But when is a customer really a customer, if they are being billed only for usage?

Variable pricing is great for customers (with the exception of some enterprises who require fixed budget amounts, but we won’t go into that). They pay for only what they use, and don’t have wasted costs should their needs change. And many pricing plans automatically give discounted rates as their usage increases. Customers just pay for the number of Emails, API calls, server instances etc. that they consume.

As an entrepreneur or sales person, however, you need to know what your customers are doing and what they are paying. If there are no contracts signed for commitment periods, and customers only pay if / when they use your product, do you ever really “have” a customer?

The answer is yes. But you need more visibility into customer usage, all the time.

When you look at an individual customer, you need to be able to see their usage — current, recent, average, trending and all time aggregate. Ideally you can see this data in real-time or with a minimal delay (so everyone on your team can trust it to be accurate). And you want the usage data in the same location as all the other information about the customer.

A best practice is to integrate this data into a CRM system like Salesforce, so that anyone looking at the customer could see their usage and bills, and understand the patterns and trends. If you don’t use a CRM system, then you will need to build a lot of the reporting and alerting logic into your application itself, which can be a distraction from building customer facing product features.

Making the usage data easily accessible, on an individual customer basis and in aggregate, is a key step in getting visibility into your customers. Reporting and graphing usage patterns, and triggering actions based on changes in usage, will allow you to be responsive to your customers

The curse of utility pricing can also be the great opportunity.

Needing to stay on top of customer usage can be a great opportunity however. You can delivery better customer service and drive more revenue by staying in tune with your customer’s usage. Here are 3 key examples:

Preventing Churn
Rather than waiting until the end of a contract to find out that the customer had not used your product in 6 months and is going to cancel, staying on top of customer usage can help you proactively identify potential churn candidates. If a customer’s usage is trending downward, has dropped more than X%, or ceased entirely, you can be alerted and take action to contact the customer and rectify the situation.

Upselling / Preventing Overage
Many billing models offer a hybrid of usage-based with plans that customers commit to in order to get a lower per-unit cost. This can be good for both parties, as you get some guarantee of revenue, while customers who have predictable needs lock in at a lower rate. But if customers have variable needs, or there are changes in their business which cause spikes in usage, trouble can ensue.

If you are closely tracking customer usage, your sales team can use changes in demand to very efficiently contact customers and upsell them to higher plans, resulting in more revenue. At the same time, you deliver a better customer experience, by not blindsiding customers with large overage bills that they feel they should have been alerted to.

Customer Modeling
The final, and perhaps most exciting, opportunity that can come from having visibility into customer usage patterns for utility pricing is the ability to model and segment your customer base by their actual, real-time behavior. For example, once you have identified usage patterns that generally define certain customer types, you can create reports and views in your CRM system that automatically move customers into different target groups as the usage data related to their account changes.

What do the usage patterns, and historical ramp up, of your best customers look like? With this data, it is easy to be alerted to new customers whose usage matches those top $ value customers, and who should be prioritized by sales.

And what do the usage patterns look like for those who stay on a free developer plan, and almost never upgrade to paid accounts? Best to leave those to occasional contact through your marketing automation systems, they probably don’t want to be bothered anyway.

In the end, utility or pay as you go pricing presents both challenges and opportunities. If your user data lives in a silo somewhere, then whether someone is an active customer, and whether they are moving up or moving out, can be something of a black box. But if one integrates customer usage data into a CRM system where it lives alongside all the other data used by sales and marketing teams, then it can be an enabler for better efficiency, happier customers, and more revenue.


CloudAmp helps software companies centralize  customer engagement, usage and account data in their CRM.

Increase your conversion rates, customer retention, and revenue by gaining real-time visibility into how customers are using your App, directly within Salesforce.

Flying Blind in the Freemium Funnel

You launched your new App. Hundreds, maybe even thousands of people signed up for a trial or free plan. 

Congratulations. Now what? 

When you had a few beta users, you could communicate individually with them, and have a pretty good idea about how everyone was using your product. Now you are looking at a huge list of anonymous customers, and wondering where to start. 

If yours is like most companies, you push your sales team (or yourself in an early-stage startup) to manually review more accounts, and do more introductory emails and phone calls. 

Or marketing establishes a timeline of how customers would normally start using your product, and sends out mass emails based on their best guess of where customers should statistically be in that process, based on when they signed up. 

Ok. But can’t we do better? 

What if you could see exactly how each individual customer was using your app, directly within your CRM system? And what if all of their contact information, account information, and usage metrics updated in real-time? 

Then you could know exactly where each customer was in the freemium funnel.  

Who had signed up, who has started using the product. Which customers were using the product in a way that they were likely to stay on the free plan forever. Which customers are starting to use the product in a way that indicates they should upgrade to paid, and which ones are stopping or decreasing their usage.


Once you have that visibility, all sorts of things become possible.


You can segment customers between marketing and sales, updating your sales team’s CRM views so that customers who have just signed up or who aren’t seriously using the product don’t clutter their prospect view. Let email campaigns from marketing automation handle those guys. But if a new customer’s usage suddenly matches your best customers usage, you can now trigger a follow up task for a sales rep right away.

This actual usage data is very useful for a free trial, but critical for the freemium (or permanently free) plan, where adoption is often not as time-defined since there is no pressure to start paying. Learning the usage metrics of when customers might be likely to upgrade to paid, and then making sure your follow up efforts mirror those events will have a significant impact on conversions.
There is no punchline to this story, just a happy ending. 

Of course, CloudAmp has built a product which does precisely all of the above. By integrating data from the back end of your SaaS application directly into Salesforce (other CRMs coming soon), you get the real time understanding of how each individual user is engaged with your product. And your team can segment your users based on their actual behavior at that point in time, for targeted conversations that produce revenue. 

Stop flying blind in the freemium funnel, and get some visibility into your users today.



CloudAmp helps software companies centralize  customer engagement, usage and account data in their CRM.

Increase your conversion rates, customer retention, and revenue by gaining real-time visibility into how customers are using your App, directly within Salesforce.

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