Transforming Sales with Predictive Analytics



hi my name is josh wanna and welcome to selling pot TV today we have the pleasure of meeting with lisa phien della and she's the founder and CEO of refocus analytics welcome Lisa welcome dear hard welcome to Atlanta thank you you a data specialist and I'd like you a answer to a one quick question okay why is data so important all of a sudden and analytics well data as you know and as we read about is growing at an amazing rate every day and the statistics are staggering the challenge is is that the resources and skills to really address and evaluate data aren't growing at the same rate and so there's a big gap in the market place around how organizations can actually use this vast amount of information so tell us at a 35,000 foot level when you look down on the territory that people can map with data what should I be looking at well when I you know the territory I map is really data for sales and marketing purposes and we tend to look at it from four perspectives one is really understanding your customer segments so this whole notion of customer segmentation and getting really smart about the markets you're trying to serve the second is around lead scoring and lead prioritization so what are the chances that a lead will generate an amount of revenue for a business or khlo become a closed one deal the third area is revenue modeling which is if I have an account what are the chances that that account will actually generate revenue for my business and in the fourth area that people often forget about is around customer retention and attrition and this is where we asked the question about is a customer likely to stay with your business or are they highly likely to leave and as a result how do you deal with that so let's drill down on to and customer segmentation and lead scoring okay what is the difference between diagnostic analytics here and predictive analytics so diagnostic analytics are really point in time snapshots if you're using a CRM system you're very familiar with diagnostic analytics because it's a report look it's a point in time a piece of information that says I my reps conducted this many sales calls last month right but that doesn't tell you anything about how you should go forward right predictive analytics takes all the information that's happened in the past and uses that information to predict the likelihood that something will happen in the future right and that's the difference between diagnostic and predictive and our goal really a tree focus is to help organizations turn make data come to life let's say we look at past leads that went through the funnel and they closed and then you look at market data and see what the market needs how do you analyze that what tools do you use what we do is we take multiple data sources we combine them into a common data set we applied mathematical algorithms and tools and then the result is usually a score or a numeric value that tells a sales organization how they should take action going forward so then you can tell the sales manager to send salespeople only off the leads that have a certain scored right exactly right so in the lead scoring scenario if a rep has 500 leads in their portfolio and only 300 of a matter but we still want to rank the 300 which is the first one they should go after it so a lot of sales managers that I talked to they want to go for reactive organization to becoming a predictive sales organization when you talk about revenue modeling how does that work so revenue modeling is a great a great opportunity and for example I have a client who we built a customer segmentation system for them so they could really better understand their market and the second step in the process was modeling for revenue so in this particular example we took five different data sources we looked at them to determine what the chances are of an account generating a certain amount of revenue and then those those predictions were layered back into the selling process and the sales managers then managed to that right they were working with their reps to make sure they were focused on the highest revenue generating opportunities and in this example one of the interesting things was at because we had segmented their business we were able to create selling strategies by segments so they were giving the right message to the buyer so they were most likely to buy the product so actually in this particular case their their revenue increased and actually sales rep satisfaction was great so what are you saying is you not only need to analyze buying behavior but also selling behavior and then moved to prescriptive analytics and telling salespeople right how to approach this my friends second right so you you know the predictive piece is really important but you've got a layer on the prescriptive pleat piece as well which is then how do you execute against it because just the analytics for the sake of analytics is just right more data right right and we're trying to make sense of data well thank you Lisa for anybody who would like to get more information click on the link below

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