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first_img – / 9 Derrick Hall satisfied with D-backs’ buying and selling The Cardinals still haven’t decided on who will start Week 1 against Washington.But USA Today believes whoever isn’t playing form a very capable group of backups.USA Today’s Nate Davis ranked every NFL’s team quarterback situation should the starter get injured. With a current group of Sam Bradford, Josh Rosen and Mike Glennon the Cardinals have a good mix of veterans and a potential franchise quarterback in Rosen. Former Cardinals kicker Phil Dawson retires (AP Photos) The 5: Takeaways from the Coyotes’ introduction of Alex Meruelocenter_img Top Stories 3 Comments   Share   They’re either going to have a highly capable backup (Sam Bradford) or a sublimely talented rookie (Josh Rosen) holding the clipboard for a team that has plenty of talent throughout the roster. Erstwhile Bears starter Mike Glennon could struggle to stick.It’s expected that Bradford will be the starter at the beginning of the season, but his injury history underscores the importance for the Cardinals to have capable backups.Related LinksCardinals RB David Johnson excluded from NFL Top 100 listBradford spent the last two season’s with the Vikings, starting 15 games two seasons ago. Bradford missed all but two games last season with a knee injury.Rosen had his battles with concussions at UCLA, including missing more than half of his sophomore season. It will be important for the Cardinals to protect Rosen in order to get the most of his potential.Heading into Steve Wilks’ first season as head coach, the expectation is that the offense will be run through running back David Johnson, assuming he’s healthy.Finishing first on Davis’ list were the Philadelphia Eagles, who notably have former Arizona Wildcat and reigning Super Bowl champion Nick Foles backing up Carson Wentz.Wilks’ former team, the Carolina Panthers, finished last in Davis’ rankings. Grace expects Greinke trade to have emotional impactlast_img read more

Lead Scoring Models Assigning Point Values

first_imgIn my last post, I covered how to start building a lead scoring model and at what point in a company’s lifecycle it should be done (once the company has reached the expansion stage, is focused on sales and marketing, and has a high inflow of leads). To quickly review, the first step is to put together a data set of lead attributes (search phrase used, number of pages viewed, country of origin, etc.) along with whether the lead converted to an opportunity or sale. After the data set is assembled, the second step is to analyze each attribute’s (independent variable’s) correlation with conversion (the dependent variable). The idea in this step is to find attributes that are either strong positive or negative predictors of whether a lead will convert. For example, if leads that trialed the product convert at a higher rate than average, this would be a strong positive predictor of conversion. Alternatively, if leads that only viewed one page of the web converted at a much lower rate than average, this would be a good negative predictor of conversion.After each attribute’s correlation with conversion has been analyzed, the third step is to assign point values for each attribute (positive or negative). If you are creating a manual lead scoring model (as opposed to using multiple regression software to get attribute/variable point values), it is important to be consistent. A simple, consistent method of assigning point values is taking the overall conversion rate and subtracting it from the conversion rate of leads with a specific attribute. For example, if the overall lead to opportunity conversion rate is 10%, and the conversion rate for leads that have trialed the product is 25%, add 15 points to every lead that has trialed the product (25 minus 10). If you find that leads that come from Google Adwords campaigns only convert at 3%, deduct 7 points from every Google Adwords lead (3 minus 10).When manually assigning point values, there are some pitfalls that should be avoided – namely assigning point values to attributes that have low sample sizes and assigning point values to attributes that are highly correlated with one another (multicollinearity). I will cover both in more detail next week.AddThis Sharing ButtonsShare to FacebookFacebookShare to TwitterTwitterShare to PrintPrintShare to EmailEmailShare to MoreAddThislast_img read more