The New Science for Optimizing your Tech Sales Reach with N3, InsideSales + SiriusDecisions

Editor's Notes

• #10: What are the key disciplines to master to optimize teleprospecting effectiveness? We have identified eight factors that must be mastered in order to optimize a b-to-b lead development function. The eight factors can be segregated into three foundational elements, four building blocks and one overarching discipline. The foundational factors. The model is built on a foundation of three pillars: the culture of the lead development organisation, the design of the lead development jobs, and the measurements that are established to help characterize performance and diagnose opportunities to optimize. The building blocks. On top of the three foundational factors sit four key building blocks. The first of these, LDR selection, establishes how to select candidates who will be likely to succeed in an LDR role. The second, LDR development, describes an approach for organizing and prioritizing subjects for mastery by the LDRs. Next, we consider how LDR roles should be executed, including the tools, processes and systems to use to enable consistently excellent execution. Finally, we consider the importance of selecting calling targets thoughtfully. The overarching discipline. The last factor in the model, management, builds on the foundational elements of culture, job design and measurement to ensure that consistent application of best practices for governing both the effort and the quality of work conducted by lead development.
• #12: Ok, so we’ve talked about a lot to do in preparing so far, but we haven’t really talked about how to prepare the human resources. So, bit of background… Our data shows that 95% of all calls end with a neutral or negative outcome. So, you know a moment ago I was saying that you should invest in predictive analytics to improve the ability of your team to call at the right time, and to inves the right amount of energy. Think of it, 95% of all your LDR’s time produces nothing or a negative outcome. = One of the reasons it is so poor is that the standard practice is to hit voice mail and either leave one or hang up without leaving one, both of which are terrible choices when you don’t know anything more about the prospect or account. Your team has to get out of voice mail jail and find someone, anyone, to talk to who can provide some intel on the account, the contact. Our data also shows that reps who continually make just a little bit more effort on each contact attempt are the ones who proeduce the best results. = Finally, the very worst thing your team can do is bully or play tricks on gatekeepers. You all know what I mean. You wouldn’t want it done to you. Don’t allow your organization to do it to others. It makes people angry, and even if they are an ideal prospects, you will lose far more than you gain.
• #13: Example of the need for more sophisticated measurement
• #15: <<>> Put the context Isabel: The situation is slightly better in Germany where 52% of respondents say they track both leading and lagging diagnostic indicators. (range is from 33% to 52%) Here’s what we advise. Begin with the goal – producing qualified leads. It could be TQLs or TGLs, so we use an x to represent both. Working backward from there, we know that we have to engage prospects in real conversations to produce leads, so let’s make sure we are counting that. Your team can report that in their call dispositions. And to get genuine dialogs, you have to be talking to other humans. The data show that reps who have more total conversations also produce the most leads, so let’s make sure we are counting that. One of the primary reasons we don’t have conversations is that gatekeepers, be they assistants, subordinates in the department – whoever – refuse to help us reach the right person. We call these blocks. And they indicate a coaching oppportunity around clearly explaining who you are, and why you are worthy of someone’s time. Finally, contact attempts. That is typically all anyone is measuring today. Each measure should be evaluated relative to what came before. Variation in performance at each conversion point will indicate a specific problem that can be solved for the rep.
• #17: The technological innovations of the last several years have really changed how we think about and execute prospect outreach. So, we start with our prospector, and we now have a varied toolset, including linkedin, social, email, phone, and all powered by predictive. So, instaed of just talking about email or social, I’ll momentarily raise the level back up to the theoretical to talk about types of communications, and I think you’ll see why that matters. In the fields of communications and psychology, researhers distinguish two broad categories of communications. They are either synchronous, meaning that they involve a live 2-way exchange in real time, or there are asynchronous, meaning there is or may be a gap between receipt and response. The telephone is a purely synchronous medium, and synchronous media are disruptive. They are absolutely the best for a rich, productive conversation, but the disruptive nature makes them less than idea for first communications in many respects. So,….
• #18: Asynchronous communications are definitely in vogue, particularly as introductions and as a way of doing some quick opportunity research. Ultimately, we want to get to a synchronous exchange – we need to have a conversation. But, we may need to start that conversations asynchronously. Here are some tips for doing that. ….
• #19: Now we shift to the preparation phase, which is really about establishing and sequencing the tactics. = And it starts with selecting prospects to whom we are going to direct our outreach. The most common sources are Known visitors, generally kept in marketing automation Retired prospects, recycled from both CRM and MAP Because these are known prospects, we can market to them directly. What is different now is that we want to run these through a predictive filter to surface and prioritize the best prospects. The first step will be to target them with messages via email Now, we can also include anonymous prospects from our MAP, through targeted display advertising to high propensity IP address. That’s a mouthful, so what does that mean. It means that now, we can run anonymous visitors through a predictive filter, often determining which companies they work for via reverse IP lookup. From there, they can be run through predictive models that determine whether and to what extent they are good fits to be targeted by display advertising. Finally, we can source net new prospects (accounts and contacts) that can be targed through these digital means = Only once we've done that do we begin our human directed outreach - that's the part involving people. This is still primarily an email and phone based project, but InMail, twitter and other social communities also present options for reaching out directly for one on one communications with prospects.
• #20: So, we've mentioned predictive already, so I'd like to tie it all together for you and talk about the five specific use cases for predictive analytics in small net fishing, which we've already mentioned. = As before, we'll look at this across our four phases, and the three use cases of small net fishing. = And when it comes to predictive, marketing drive and rapid response share predictive use cases, so we'll treat them together. In both, predictive models are used to build ideal customer models, including both profile or fit and behavior. We break this isn't two distinct uses, prioriitizing existing or known prospects, and sourcing net new. Another use of predictive is what we call Tactic Matching. Given enough information about your prospects and their behaviors and attitudes, it becomes possible for predictive models to not only determine what the next best message is, but also what medium should be used to deliver it. Now, this is an advanced use case. If you don't have lots of experience - think, 1000s of records in marketing automation and CRM, you won't yet be able to take advantage of this use case. But, many of you do. And if you do, a predictive provider may well be able to help tie next best message and tactic to help plan your outreach. = Next we have contact engagement. So, we've selected and prioritized the accounts we want to reach out to, but contact engagement takes this a step deeper, as helps us understand which contacts are most likely to pick up the phone when we call, at what times we should try them, and how much effort is the right amount. This is last mile spredictive in a sense. But think of this. The most expensive resource in this chain of events will be the LDRs. How they apply their effort is critical, adn is typically left to their own discretion. This is a big mistake, especially when we can know what the right kind, amount and timing of effort should be. = Finally, we have the account monitoring function I mentioned earlier. We've already talked about it, so won't go much vurther here, only to say, think how valuable it would be to know that one of your named accounts is showing a spike of research activity in web searches, social media interactions and follows, etc., researching just the issue you address with your solutions? = Finally, what is also hugely beneficial about predictive is that most predictive models are or can be continusously learning models. when results are fed back into the machine, it continues to refine its output.
• #21: One of the most straightforward uses of data to drive productive is to use it to figure out When to call How often to call And even what to say when you call prospects. <<Build>> The first is quite straightforward and rely heavily on third party historical data to determine the optimal time to reach out to prospects. <<Build>> But it should not be news to anyone in this audience that there is an optimal level of effort to apply to reaching prospects, and this can be a game changer for organizations with LDR teams. Knowing how much is enough can both improve productivity and help prevent annoying prospects. <<Build>> In the final category, companies are popping up now that specialize in reading the public pronouncements of people and making scientifically valid assessments about their personalities and communications styles that can help organizations structure communications, both live and digital, in a way that is more likely to connect with the prospect. <<Build>>
• #22: How does predictive help to get the right number and type of inquiries? And we want And so we want to start by showing how a predictive sourcing lookalike model works, and as we’ll see, many of the other applications we will look at today share a common dataset that they work from, and do very similar things. <<Build>> look-alike modeling starts with two sets of records. The first is a set of companies that we tried to sell to but failed, and the other is a matched set where we succeeded. And we have the dataset of predictor variables, <<Build>> <<Build>> <<Build>> <<Build>> <<Build>> this is everything from our internal MAP and SFA data to the huge universe of third party data <<Build>> And we're going to try to build a template with this data. And in essence what this predictive application is doing is pouring all that data over the two positive and negative data sets, <<Build>> to see if there is some data that reliably sticks to the companies where we were successful, but does not stick to the losses. <<Build>> And if we find that, These becomes markers that identify good prospects. <<Build>> And we can take that template and go match new prospects against it. Ones that look substantially like our Wins template are sourced so that we can go market and sell to them. <<Build>>
• #23: So by now, this should look familiar. We have our model with all the familiar data elements, and it’s essentially the same model with the same data. In this case, we may rely more heavily on data in our own systems – marketing automation and SFA. But, not necessarily. Here, we are assuming that we have some prospects consuming our content and in our MAP. Now, the reason we don’t call this lead scoring is that with predictive we are often not primarily interested in the actions of one person, but we’re interested in the aggregate of actions of multiple people. So, just like back with intent data, we’re interested in knowing whether more than one person in an account is active. <<Build>> So, we take our prospect – whether it’s an individual or a group of individuals at an account, and we drop it into our model. Here the model may have the very same profile we had before when we were doing look-alike modeling. <<Build>> Here, we have our ideal prospect, but rather than go out into the world and match new prospects to it, we’re going to <<Build>> compare our existing prospects to that ideal and give that prospect a score that tells us how well it corresponds to the ideal. <<Build>> Prospects that are a good match are kicked out to an LDR or a sales to take action on. <<Build>> One of the principle benefits of a prioritization model of this kind is that you can take the results you get following up on those prospects and feed that back into the model to continually refine the model. <<Build>>