35 SaaS Marketing Products @ 1 Startup

Yesterday I was talking to the head of marketing at a fast-growing, <100 person SaaS startup. We were talking about the modern marketing stack and he mentioned that they pay for 35 different SaaS products. Yes, 35 different marketing apps at one small business. Some of the app categories included marketing automation, social media management, A/B testing, SEO analytics, etc.

Here are a few questions that come to mind:

  • Is there an upper limit to how many marketing apps a small business will use?
  • When does app fatigue set in?
  • How many are apps require daily work vs ones that are set it and forget it?
  • How is reporting done across so many apps?

SaaS is unique in that once the business has $500,000 in recurring revenue, it’s hard to kill. Thus, there’s a huge cottage industry of SaaS marketing apps that provide value. It’ll be interesting to watch the industry over time and see how it plays out. My prediction: there’s no upper limit of marketing apps and we’ll keep seeing more and more.

What else? What are some more thoughts on the idea that there are 35 SaaS marketing products at one small business?

4 Reasons to Add an SDR Team

Earlier today I was talking with a couple of sales leaders about Sales Development Reps (SDRs). One leader was a big proponent of SDRs and the other didn’t have any experience with them. From the discussion, I took away four reasons to add an SDR team:

  1. SDRs help make the more expensive, and more experienced, account executives more productive
  2. SDRs, with their focus on appointment setting, are more efficient than full-stack sales reps, which are spread thin across a variety of functions (setting appointments, doing discovery calls, facilitating meetings, delivering proposals, and closing deals)
  3. SDRs are effectively a training ground for future account executives thereby acting as a talent development pipeline
  4. SDRs are also a training ground for non-sales roles like support and customer success

Startups should evaluate these four reasons to add an SDR team, and once a positive decision is made, review these 7 Quick SDR Tips for Startups.

What else? What are some more reasons to add an SDR team?

Hindsight, Insight, and Foresight

Lately I’ve been thinking more about about going beyond merely looking at backward-looking data and metrics (e.g. The Definitive List of Weekly Operational Metrics for SaaS Startups) and learning how historical data can be used to inform what will happen (predictive analytics). Derek Kane has a Slideshare titled Building and Sustaining Predictive Analytics Capabilities. On slide 26 he defines hindsight, insight, and foresight.

  • Hindsight – What is happening?
  • Insight – Why is it happening?
  • Foresight – What will happen? What should happen?

As startups mature and improve their operational excellence, insight and foresight become logical additions to the weekly business review. Entrepreneurs would do well so start asking the questions “Why is it happening?” and “What will happen?”

What else? What are some more thoughts on hindsight, insight, and foresight?

7 Quick SDR Tips for Startups

As more startups hire sales development reps (SDRs) to set appointments and schedule demos, it’s important to learn best practices and increase the chance of success. Building an SDR team, like any job function, takes time to figure out what does, and doesn’t work. Thankfully, there are a number of great resources online. Here are seven quick SDR tips:

  1. Start by reading the Predictable Revenue book
  2. Build a sales playbook
  3. Hire sales reps ahead of plan
  4. Always hire reps in pairs
  5. Make sure inside sales makes sense
  6. Require a written assessment in the hiring process
  7. Map out the sales process

Use these seven SDR tips and build a great team. The SDR function is the most important sales process innovation in the last 10 years.

What else? What are some more SDR tips for startups?

Build a Sales Playbook

One of the first things a new sales leader (or entrepreneur running sales) should do is build a sales playbook. A sales playbook, put simply, is a central resource for tracking everything from the basics, like the elevator pitch, to the more advanced items, like differentiating from specific competitors. With more knowledge and training, sales reps speak more confidently and intelligently, helping win more deals.

To start, make a Google Doc sales playbook and include these items:

  • Corporate information
  • Sales pitch
  • Elevator pitch
  • Market space
  • Recent trends
  • Target customer
  • Types of buyers
  • Features and benefits
  • List of references
  • Sales process
  • CRM process
  • Competitors and differentiators
  • Objection handling
  • Glossary

Revisit the playbook on a weekly basis and ensure that the team contributes to it. With sales, the more you know, the more you sell.

What else? What are some more thoughts on building a sales playbook?

Analyzing Data Over Time

Every entrepreneur I know loves to analyze data and metrics about their business over time. How’s our revenue growing? How many daily active users are we averaging? Only, the data is often in summary form in a spreadsheet making it hard to analyze. Eventually, an analytics and reporting system is necessary to better analyze data over time and present it in an actionable manner.

Here are some common questions to ask when analyzing data over time:

  • How are we doing this month/quarter compared to this time last month/quarter (ideally with a chart showing both lines)?
  • What’s our trailing 30-day average as measured on a daily/weekly basis?
  • What’s the rate of change on a weekly/monthly/quarterly basis (are we accelerating or decelerating)?
  • What’s our expected results for the rest of this month/quarter based on the previous data and corresponding results (predictive analytics)?

Analyzing data over time is a critical part of every weekly leadership team. Use analytics and data platforms to automate the collection of data and generation of reports that show both the metrics and more detailed analysis.

What else? What are some other common questions to ask when analyzing data over time?