Bad data is bad business. Indeed, organisations believe poor data quality to be responsible for an average of $15m each year in lost earnings, according to research by Gartner. Here we reveal the cost of bad data and why it might be time to clean up your act.
Those of you of a certain age may remember The Buggles’ hit, ‘Video Killed the Radio Star’. Since, according to Wikipedia, it was the first video shown on MTV (back in 1981), I realise that is not most of you. However, the point of the song is clear just from the title – new technology supplants the old and mercilessly discards it.
However, radio was not killed by video, or anything else for that matter. It survived and thrived as more people travelled by car, talk radio took off in the 80s and Sirius XM now enables us to listen to virtually anything anywhere.
As far as I know, there hasn’t been a hit single lamenting the death of outbound telemarketing to develop marketing leads. But for many, it epitomises yesterday’s marketing – intrusive, untargeted, expensive. (Just to be clear, I am talking about telemarketing in the context of outbound lead generation – not bottom of the funnel inside sales.)
Why did outbound telemarketing fall out of favour? I have identified five main reasons:
- Firstly, the gradual adoption of inbound marketing, enabled by wider use of the internet by buyers, a better understanding of the need to track the customer journey, and the increasing use of marketing automation tools. And that, of course, is by no means an exhaustive list. It became an expectation that provided a proper demand generation process was set up – personas, content, data, a strong nurture stream – the prospect would come to the brand at least, in the early stages of the buying cycle, and remain engaged, without the telemarketing push. All that is a big ‘If’, of course.
- Most calling programs used a combination of in-house and 3rd party data. All too often, this was of poor quality with limited segmentation and little or no behavioural overlays. And, if it did have them, they reflected responses to past campaigns rather than future plans. In combination with the other factors, results became increasingly disappointing.
- Decision making patterns were becoming more complex. So a ‘one and done’ telephone conversation didn’t necessarily qualify the lead effectively. It was taking multiple calls within the same organisation to make progress.
- Time – it became longer and longer to track people down and have that substantive conversation. Prospects were simply becoming more unwilling to spend time on the phone. There was probably some cross-over between poor experience with consumer telemarketing at home and the business world.
- Cost – calling is labour intensive and therefore expensive. It takes up a significant amount of dollars, no matter how well it is done. Those dollars were increasingly being spent on the infrastructure and tools needed to create the inbound process as well as a move to programmatic display advertising.
However, telemarketing has survived because of two benefits not easily replaced:
- Inefficient though it may be, it can deliver leads at scale. To some extent, it is simply a question of adding resources. And the marketer controls the pace – it’s a very proactive medium while your budget lasts.
- Results are immediate and you can make resource and other adjustments in real-time. If you are running behind your target you can change script, data set, or callers relatively quickly. Or, at the very least, set new expectations.
So, in the face of the difficulties almost all companies have in calibrating inbound marketing, and the constant pressure for leads, telemarketing has continued to be part of the mix. Nonetheless, its long-term future has until recently been seemed in doubt.
But now a number of new and existing providers have come along with new or enhanced solutions – with predictive analytics and purchase intent either embedded into the data sets or offered as an add-on. As these are adopted, it may be that the combination of selective targeting resulting from this data, with better content resulting from the focus on nurture streams, will combine with the speed of telemarketing to accelerate sales.
Whereas in the past you had to call first to find the right target or opportunity, you can now jump that stage and call with a much greater certainty that the time and the target are right. A better experience all round.
In fact, the provision of these new data sets, often bundled with other demand generation services including calling, is becoming increasingly competitive. I have listed some of the companies below. The offerings can be complex, and marketers will have to choose carefully and test. It’s important to dig into the differences in methodology, coverage and geography – and, in particular, to distinguish between predictive models and purchase intent.
These are not in any order except alphabetical and certainly not a comprehensive review or comparison.
Aberdeen combines a database of installed technology with behaviour-driven buyer intent and analytics offerings including best customer profile, market opportunity mapping and prospect scores. The Lead Essentials product links the data to Aberdeen content.
Bombora also uses a mix of behavioural data and analytics, but in a different way. Its ‘Surge’ solutions will “capture and apply intent signals across the B2B web”. These are then scored at company and location level to support targeting.
Discover.org goes the human route initially, with a team of researchers who update company profiles including technology and org.charts every 60 days. Their OppAlerts product provides buyer intent based on content consumed across thousands of publishing sites.
Dun and Bradstreet offers the resources of both the enterprise company database and Hoovers in-depth information. D&B Hoovers Premium provides “ideal profiles” and “business signals”.
InsideSales.com has a range of products and services that use models to score the most promising leads, accounts and opportunities. It matches data in a client’s CRM to its own database of sales interactions and then appends 3rd party firmographics. It can also link to their predictive dialler tool.
Mintigo delivers lead scoring models based on predictive analytics as well as “marketing indicators” taken from the web and then linked to data in the client’s own CRM or MAP platforms. Mintigo can also cleanse this data from their own sources.
RainKing has a SaaS-based solution that enables marketers to access profiles which include installed technology, spending and investment signals across their database.
Tech Target tracks buyer behaviour in multiple categories of technology through responses to editorial content. Their purchase intents are based on “relevant content consumption over time”.
Marketers therefore have a lot of alternatives to test. What are the next steps you should take in deciding how and when to integrate calling into the demand generation mix using these data options? Here are three crucial steps:
- Review which data attributes available will give most lift to your current operations and data feed – and verify through testing.
- Make sure in your planning that the intelligent data can and will be leveraged promptly – remember, the goal here is to accelerate the revenue drive.
- Don’t neglect the other variables in a successful calling program, including and especially the team of agents, their training and leadership.
Finally, remember that all demand generation is an iterative process and be alert to making swift changes as results come in. But I am confident that combining strong data with well-executed telemarketing campaigns will indeed speed up the revenue cycle.
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