The 3-Step Startup Marketing Framework

Hiten Shah has a great post up titled The 3-Step Startup Marketing Framework where he outlines the process he used to help grow popular startup Kissmetrics. Here are the three steps:

  1. Identify your target customer by understanding:
    • What your product does
    • The problem your product solves
    • Who wants this problem solved
  2. To find out where your target audience hangs out:
    • Create a master list of potential places
    • Establish criteria for ideal marketing channels
    • Vet your list according to those criteria
  3. To engage with your customer
    • Identify your method of engagement
    • Expand as far as this method allows
    • Confine your reach only to the target audience
    • Aim to deliver a high amount of value

Go read The 3-Step Startup Marketing Framework and follow his process.

What else? What are some more thoughts on this startup marketing framework?

Q4’s Sales Results Inform Next Year’s Budget

As entrepreneurs are putting the last minute, final touches on the 2017 operating plan, there’s an important point that is often overlooked: Q4’s sales results inform next year’s budget. Meaning, entrepreneurs are an optimistic bunch and like to make big plans using a combination of a bottom-up and top-down sales forecast. Only, these forecasts are made before Q4 has finished, and Q4 is often the best sales quarter as many companies make purchases at the end of the year with fresh budget in place for the new year.

Here are a few thoughts on Q4’s sales results informing next year’s budget:

  • New sales drives a number of other functions like the number of people needed for support, customer success, customer implementations/on-boarding, etc. such that Q4 sales results affects hiring plans for the new year
  • If Q4 sales exceed plan or are below plan, that means there’s a higher/lower run-rate to start the new year, and budgets will need to be adjusted
  • If Q4 sales are off plan, good or bad, that’ll reset sales expectations for Q1 of the next year higher or lower

As much as budgets and operating plans for the next year are reviewed and finalized, the reality is that they’re built around hitting sales expectations for Q4. If Q4’s sales are better or worse than expected, budgets should be revised.

What else? What are some more thoughts on how Q4’s sales results inform next year’s budget?

Marketing Orchestration Platform

After sharing the story of 27 SaaS Products for the Marketing Department with an entrepreneur, I got to wondering if there’s going to emerge a new category of product: marketing orchestration platform. Today, marketing departments run as a federation of specialized functions managed by a central project management app like Trello or Asana. Only, the project management app is just for the projects, not for coordinating all the moving parts.

Here’s what a marketing orchestration platform might do:

  • Connect via APIs to the major applications used by marketing
  • Automatically show all campaigns/programs/processes running
  • Collect all the relevant metrics and data for processing and visualization
  • Apply machine learning to the data and make recommendations
  • Facilitate coordination and timing of on-going programs
  • Profit!

Then again, it might be too complicated and cumbersome and make all that happen elegantly. If someone can pull it off, it feels like a gap in the market. I’m curious to see if a standalone product emerges or if the marketing automation vendors get better at becoming a platform that other apps integrate with (the same way Salesforce.com has done) and marketing automation becomes the marketing orchestration hub.

What else? What are some more thoughts on a marketing orchestration platform?

Gaps in Marketing Technology

Recently I was talking with an entrepreneur about marketing technology — a space he knows well — and he said that because of so much capital going into the space, there aren’t that many gaps. Hmm, I thought, there are a number of gaps where leaders haven’t emerged. Most segments have vendors in them but that doesn’t mean a group of tier 1 vendors have emerged.

Here are some big picture gaps in marketing technology:

  • Simple Marketing Automation – Marketing automation is powerful, valuable, and too complicated for many marketers. There’s plenty of opportunity in certain segments.
  • Full Account-Based Marketing – Lots of vendors are doing parts of the puzzle but there’s not a comprehensive solution. This market is harder than it looks but there are still big gaps.
  • Deep Online Behavior Understanding – People are tracked online much more than they realize. Only, beyond the basics (which are 100x better than no data), there isn’t deep understanding of user behavior and patterns.
  • Marketing Orchestration – Marketing has an incredible number of tools (see 27 SaaS tools in the marketing department). What system orchestrates them all?

These are a few of the gaps in MarTech that I expect to be addressed over the next five years.

What else? What are some other gaps in the marketing technology landscape?

Monthly Sales Quotas

Historically, sales reps were assigned quarterly quotas and expected to hit their number. Only, as inside sales and web-based sales have grown in popularity, the quota time frame hasn’t changed with it. Today, high velocity sales reps need monthly, not quarterly, quotas.

Here are a few benefits of monthly sales quotas:

  • Monthly quotas keep the focus on a steady stream of deals, not the more common lumping of deals at the end of the quarter
  • Monthly quotas keep the activity volume high as there’s a need to ensure open opportunities at all stages of the funnel
  • Monthly quotas make it clear where every rep stands, every week, with regard to deals won, not just weighted pipeline, resulting in a more predictable revenue stream

Entrepreneurs would do well to implement monthly sales quotas and develop a modern sales team.

What else? What are some more thoughts on monthly sales quotas?

Predictive Sales and Marketing Data Sources

Continuing with yesterday’s post on Predictive Sales and Marketing Technologies, one of the areas to go deeper is the sources of data that can be used for scoring lead/contacts as well as building out the lookalike companies for targeting. One of the reasons predictive sales and marketing technologies are so good now is that number of quality data sources available is much larger than 10 years ago.

Here are a few of the predictive sales and marketing data sources:

  • Demographic – Different personas with attributes like job title are valuable when building predictive models
  • Firmographic – Characteristics of the company like size, industry, and location are key data points
  • Social – Significant amounts of publicly available information on social media include location, frequency, interests, and more
  • Technographic – Technologies implemented in a company and on their website (e.g. web server, CMS, marketing automation vendor, etc.) provide a unique profile of the business
  • Digital Behavior – Marketing automation tracks page views, email opens, ebook downloads, and every other digital fingerprint — all useful for understanding the ideal buyer

Modern data sources combined with machine learning make predictive sales and marketing possible. Look for the quality and quantity of data sources to grow making predictive technologies that much better.

What else? What are some more data sources for predictive sales and marketing technologies?

Predictive Sales and Marketing Technologies

One of the sales and marketing technologies I’m most excited about (along with account-based sales and marketing) is the whole predictive area. At a simple level, predictive takes existing contacts and opportunities and scores them against a dynamic model based on other contacts and opportunities that became customers. Put another way: find great-fit companies that look like our existing customers so we can target them.

Account-based sales and marketing platforms (see SalesLoft and Terminus) solve the major problem of running programs in a scalable manner against hundreds (or thousands) of target accounts. Traditional marketing, and sales as the opportunity progresses through the funnel, casts a net, sees what is caught, and then works the qualified leads. Now, as a more modern approach, account-based sales and marketing goes spear fishing and proactively seeks out best-fit accounts based on a number of dimensions. Only, there’s often not an easy way to find and refine best-fit accounts — enter predictive technologies.

Here are a few thoughts on predictive sales and marketing technologies:

  • As more companies implement account-based sales and marketing platforms, predictive systems become more important to find and analyze best-fit accounts.
  • Predictive sales and marketing systems are clearly in the path of revenue.
  • Machine learning, a subset of artificial intelligence, is now more accessible with the advent of systems like AWS Machine Learning, making predictive systems more powerful.
  • Finding lookalike companies requires technology. Combing through billions of records by hand simply isn’t possible.

Look for the category of predictive sales and marketing systems to grow fast as the technology crosses the chasm and becomes more well known.

What else? What are some more thoughts on predictive sales and marketing technologies?