Go to Top

Lead Generation

Three ways to improve outbound lead generation

The first step to improving outbound lead generation is to better understand pipeline activity. An analytical approach can help sales managers to create efficiencies in sales rep activities, resulting in improved lead generation outcomes:  Sales Activity Lead assignment to reps – Placing a manageable quantity of leads in each sales reps’ queue on a timely basis so they can start calling, qualifying and closing deals without being overwhelmed. Lead disposition by reps – The “why” of determining which leads are not qualified or worth pursuing is just as important as converting a qualified lead into an opportunity. Time spent on non-sales activity – Creating action lists that can be used to prioritize, assemble and validate actionable info   How can analytics make reps more efficient with these sales activities? Lead assignment: Reps disqualify leads at a faster rate than they qualify them. The assignment of new leads can be automated based on total current active leads as a ratio of closed leads, and the relative volume of leads compared to other reps. Lead disposition: Disposition reasons that are clear, accurate and consistent can yield valuable info to fix specific problems. For example, if the reasons show bad data, go back to the vendor and get updated records …Read More

Buying prospect data: Why it may cost you 125% more than you think

Sourcing new outbound leads is a never-ending endeavor for sales and marketing. Prophesies of cold calling being dead have not come true if only for the simple reason that prospecting through all channels must be on the table to fuel the engine of sales growth. Companies typically procure lead data from four main types of leads: Internally-generated leads: Referrals, word of mouth, events, etc. Intelligence-based leads: Newsfeeds, industry alerts, personnel changes, etc. InsideView is one good example of this, but we believe LinkedIn also fits this mold (the evolution of this is exciting) Special or vertical lists: Trade associations, commerce groups, organizations operating with a geographic charter Compiled lists: The likes of D&B and InfoGroup, including credit files But as any sales or marketing manager knows, simply dumping more records on a sales rep is a thing of the past. Lead nurturing and scoring are the norm, wherein prospects are nurtured until they raise their hands as hot leads, and are then forwarded to sales. In addition, predictive modeling that identifies the most likely prospects based on the “ideal customer” profile must be part of the mix. It’s clear that the prospects at the top of the funnel – the ones you might pay for – are not sales ready. Further criteria and …Read More

Caution: Leads may be hot. Handle with care.

When you change your lead scoring and lead delivery using predictive analytics, don’t forget to train sales reps to think different as well. It’s well-known that salespeople don’t qualify leads, they disqualify them. The more leads provided, the faster leads seem to get disqualified and bounced back in the holding queue. Reasons could be due to lack of data (such as invalid or no phone number), bias (“can’t possibly be a large enough deal”), or simply attitudes (“the more I close out, the sooner I will find something that works”). This approach churns through leads, resulting in significant cost of acquisition and processing resources – both human and machine. If your reps’ closeout rate is 50%, your net cost is twice the initial cost! Marketers and sales leaders often respond by finding ways to deliver a greater number of qualified leads faster. Predictive models are often used to score and deliver ideal prospects from a larger universe into outbound lead gen programs. Predictive models increase productivity and the ROI of achieving specific outcomes such as getting appointments or sales, moving newly-acquired customers into repeat customers, and improving cross and upsell. But caution: predictive models may produce leads with characteristics that are …Read More

What to do when your sales reps think good leads are bad

Inside sales teams are at it every day, making thousands of calls to prospects, seeking an appointment or a sale. They are given scrubbed lists with contact names, job title, phone numbers and a good luck pat. On the back end, managers track calls, appointments and sales. The cycle continues when reps deplete their assigned leads and it starts over again. There are times when this well-oiled prospecting machine can under-deliver – and you may not be aware.  But there are easy fixes. Here we explore three cases, and discuss how to overcome these challenges. TESTING NEW MARKETS:  When looking at sales data, you may find strong traction among companies that don’t fit the best customer profile — or at least what the rep thinks is the best profile. This finding is usually uncovered by in-depth profiling, micro-segmentation or modeling analysis. These customers may not be among the largest customers, but you find that they purchased numerous units of products that fit specific needs.  As an example, say you are selling technology products, and religious institutions are not known as leading technology buyers. But recently we came across a church buying hundreds of iPads for one of its programs. This is opportunistic entry into a market if other religious institutions have similar programs. What you can do: …Read More

Are you doing business in 107 countries? Or 7? Data hygiene matters in predictive analytics

On a recent assignment for setting up lead generation, we took on an existing customer database to build a statistical model to score a leads database. The client does business in 7 countries, or so they said and I believed them. But they quickly added a caveat, “no one has looked at our database in awhile.” First, we looked at their billing country field. This had been an open text field in their Salesforce.com system that could be edited by just about anyone. What we found was amazing. There were so many variations for each country that unique values quickly proliferated to 107. Misspelling, case difference, punctuation and abbreviations all conspired to create many versions of the same country! Our first order of the day was to identify the obvious countries and group them, followed by corrections to the remaining data. This took 107 countries down to the correct 7. Then the country field was locked and going forward, a drop-down menu of countries is being used to prevent this proliferation from happening again. For statistical modelers, why is cleaning data important for sales intelligence? First of all, we want to point out that even a simple field can pose a challenge.  In the absence …Read More