“Social selling” is never a one-size-fits-all, turnkey proposition. Here’s a list of questions I put together for organizations who are thinking about creating or expanding a social selling program. By answering these questions—at least provisionally—an organization can create an action plan, line up people and tools, and start social selling at the scale that makes the most sense for them.
I. What’s Our Starting Point?
A. What results do we want to get?
1. Lead generation – new customers
2. Customer loyalty – current customer renewals, cross-selling
3. New/deeper relationships with Influencers – analysts, journalists, experts
I decided to share some links to a few of my favorite (mostly recent) articles and videos about #AI, aka artificial intelligence, and #ML, aka machine learning, in a post here. If anyone wants to submit additions, feel free to contribute in the comments below.
Recent overview articles about AI / Machine Learning
In an ideal world, every company’s executive leadership would project an authentic thought-leadership presence in Twitter, LinkedIn, and other digital social channels. In reality this is too time-consuming for many executives, both because of the learning curve and because of the the daily effort required to curate and personalize high quality social content. The solution I recommend—based on a number of years coaching executives in social media, and my former role at a leading social selling solutions provider — is to minimize the time required for the executive without eliminating the authenticity of the executive’s social presence. This can be accomplished by outsourcing just the right amount of executives’ workload to the combination of trusted assistants and technology, as described in this post.
I’m a marketing guy. But in my consulting days and in various other roles I have both sold and trained people to sell. My most recent in-house role, director of product marketing, had a strong sales enablement component, including attending the daily sales stand up and delivering training, content, competitive intel, tools, and strategy to my sales team.
I was a little surprised to see a post in a respected tech publication just the other day about how unfathomable machine learning is, and how unknown its impact is going to be. Agreed, machine learning is still unfamiliar to many people, and its potential is enormous. But maybe I can help demystify it a little by sharing some of my own experience applying machine learning in a real life situation.
I really dug into machine learning a few years back working on a marketing campaign concerning the use of analytics during the discovery phase of lawsuits. I got hands-on by downloading the somewhat-famous Enron emails, which I popped into a MySQL database server, and did a little poking around in them using Tableau. But what really helped me understand the power of machine learning was studying emerging e-discovery technology, culminating in a conversation with data scientist and entrepreneur Nicolas Croce (see the interview here).
Before I share what I learned, first some background for those who aren’t already familiar with what the legal profession calls “discovery”. Discovery is the process by which lawyers are permitted to obtain evidence, including documents and electronic records, from their opponents. This is permitted under civil and criminal law so that the lawyers for both sides can assemble evidence that courts need to make good decisions. In a major legal action discovery can involve literally millions of documents and equivalent types of records (images, emails, database entries, etc.). Both sides must review these documents to identify which are important and why.