Creating a high-potential Prospect List for Outbound Marketing

A well-balanced demand generation plan requires both inbound and outbound marketing components.  In the past few years, all the buzz has been around inbound marketing tactics such as website content, SEO, Google AdWords, and retargeting.  However, audience have now wised up to the fact that filling a form means enduring a series of emails and phone calls, and many are less prone to oblige. Marketers try to compensate for this slack – leading to the ongoing resurgence of outbound marketing.  The cornerstone of any outbound marketing tactic used, whether it is cold calling or list email nurturing, is the prospect list.


Uncover hidden high potential segments in your Marketing Leads database to generate more Opportunities

As a marketer, your goal is to generate more opportunities and sales.  To that end, you keep your eyes fixed in the marketing dashboard to track your progress towards the goal for the quarter. You get a sense of how things are going by the first month in the quarter.  If you are falling short, is there anything proactive you can do to meet the goals?

The answer is yes! One proactive way is to use customer-lookalike analysis on your leads database to find immediate opportunities. This is explained in a B2B marketing context below. It can be easily translated to a B2C world if that is more relevant to you.


Reducing Customer Churn in SAAS companies using Data Science

The need to reduce customer churn is now a common wisdom – it is estimated that retaining a customer is 5 to 20 times more profitable than acquiring a new customer.

Data science can be used to predict customer churn, but the approach is different for different business models – for example, a B2B SAAS model like Salesforce will need a different approach from a B2C subscription model like Netflix or a B2C non-subscription model like eBay.

For this discussion, let us focus on the churn issue for B2B SAAS companies that have annual contracts with the customers.  Our goal is to predict the likelihood of churn at the end of the contract. Assume we need to know the likelihood for the churn three months before the end of the contract so that we can take the necessary preventing actions.

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Eight ways to turbocharge your marketing performance using data science

Below are the eight ways you can turbocharge your marketing performance by applying data science:

Churn prediction: Predict likelihood of churn by current customers, especially at the end of ongoing contract. (more information)

Leads Sweet Spot: Identifying segments of nurtured leads and accounts for rapid conversion to opportunity. (more information)

Offer to Segment Matching: Identifying lead segment that is most likely to respond to a specific offer, ensuring high performance of marketing campaigns. (more information)

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Four Trends Elevating Marketing as the Key Business Engine

Many of us who have been in B2B marketing for some time know that marketing has been undervalued or plain neglected in a majority of small and mid-sized companies. As Doug Davidoff of Imagine Business Development points out: “as recently as five years ago, it was not unusual for me to meet with companies that were several hundred million in revenue who had no marketing department or focus whatsoever.”

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Five Areas Where B2B Marketers Can Leverage Big Data

Big data is now the “New New Thing” in marketing and companies big and small are scrambling on what to make out of this emerging hot subject. The articles on this topic are often stark, with the implication of disaster if companies don’t embrace big data – like, your company will be eaten alive by competitors who are more strategic and agile because they embraced big data. This post is an attempt at separating facts from hype, and to suggest key areas for your marketing organization to start tapping into big data.

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Translate Your Killer Plan into Day-to-Day Marketing Tactics

In a previous blog post, Creating a Marketing Plan That Won’t Die a Slow, Miserable Death, we looked into how to go about planning for an upcoming year or quarter. In this post, we will focus on how to bring all these planning efforts into reality through day-to-day activities. Many companies miss this crucial step. Their good plans sit on the shelf (or the server) untouched, becoming the major reason why most planning exercises go to waste.

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Creating a Marketing Plan That Won’t Die a Slow, Miserable Death

The new year is fast approaching, and many organizations are busy compiling their annual marketing plans. Many  hours are spent on dissecting data, dreaming up ideas, and putting together thick decks. Often, the marketer’s hard work will get a moment in the spotlight in the form of a presentation to the executive team. Unfortunately for many organizations, that’s where the marketing plan will begin its slow death-by-indifference, all the way to the point of irrelevance.

Why does this happen? The usual cause is one of two reasons:

1. The plan that’s been created is not aligned with business objectives for the upcoming year, or,
2. There is no framework within which to operationalize the plan for the day-to-day activities of the marketing team.

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Which Version Won? Understanding Confidence Level in Email A/B testing

Email A/B testing is generally used to select between two different variations of an email message so that the winning version can be sent to the broader population. A/B testing is a comprehensive topic; we will go into it in depth in a future blog post. In preparation for that post, we want to examine the idea of a “confidence level,” which plays a big role in interpreting A/B testing results. For example, the result of an A/B test might say “Variation B wins, with a 96% Confidence Level.” What does that mean? And how it is estimated?

Let’s look at an example. Say there of two variations of email creative that we want to test. Suppose our desired outcome is more clickthroughs. We want to identify the email variation that generates better clickthrough rates using a small list, so that we can use the winner for a bigger campaign down the line.

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Cohort Analysis: 2 Simple Steps to Better Understand Your Leads

Cohort analysis is an often-overlooked area of marketing analytics. It involves tracking a group that shares a common characteristic (a “cohort”) over a certain period of time and evaluating outcomes. In this post, we will talk about cohort analysis of leads. This involves following a group of leads which were created in a certain period, say a full quarter, until the leads become wins or losses.

A cohort analysis can provide important insights on which characteristics show that a lead has high potential for conversion to a win. These insights can then be used to determine which type of leads to focus on in the future.

Read more here.