Now that the $900 Billion COVID-19 Relief Package has approved and signed into Law. I’ve begun to unwrap it’s details beginning with an area that may not be discussed in great detail or as often as PPP or EIDL relief.
What’s new and exciting is that just over 15%, or $15 Billion, of the new program has been earmarked to help live venue operators or promoters, theatrical producers, live performing arts organization operators, museum operators, motion picture theater operators, and talent representatives who demonstrate a 25 percent reduction in revenues. Essentially, if your business utilizes ticket sales to generate receipts, then you may be eligible if you’ve experienced significant loss of revenue.
I’ve attached a very good article that details the following:
This is my Mom’s recipe for Peach Cobber, my all-time favorite dessert!
When I was younger my mother used to make the best Peach Cobbler. She’s not around any longer to shower me with such great treats, so I had to take matters into my own hands.
Below is my resume for Peach Cobbler from what I remember as I watched my mom cook up this scrumptious, fruit dessert. My take on it is that I like to make it in a bundt pan so there’s plenty to go around. And there’s something about how it presents in that uniquely designed pan.
4 15oz cans sliced peaches in heavy syrup
1.5 Cups Brown Sugar (fully packed)
2 Pillsbury Pie Crusts (1 box)
5 tablespoons unsalted butter
1 teaspoon of cinnamon
1 teaspoon of nutmeg
1 teaspoon vanilla extract
1 teaspoon Cornstarch
¾ Cup Water
Crust and Topping Ingredients
2 cups all-purpose flour
1 teaspoon baking powder
½ teaspoon of salt
5 tablespoons butter
¾ Cup of Milk
Preheat oven to 350 degrees F. and spray Bundt cake pan with Baker’s Joy non-stick spray.
Cut a hole in the center of Pie Crusts to fit over bundt circle
Layer the first Pie Crust in the base of the bundt pan
Empty contents of each can of peaches into large sauce pan.
Add 1.5 cups brown sugar, cinnamon, vanilla, nutmeg and butter
Mix cornstarch and ¾ cups of luke-warm water
Bring to boil then add cornstarch to thicken
Reduce heat and prepare topping and some dough filling.
Slice 5 tablespoons of butter into ½ inch by ½ inch cubes
To make crust use a kitchen mixer and combine flour, butter, sugar, salt, baking powder and milk until thoroughly mixed and a ball forms (batter will be similar to cookie dough).
Remove from mixer bowl and knead for a few minutes to form shape of a ball.
Pull apart a handful of dough and separate into 1 inch pieces.
Add pulled dough to peaches in saucepan and cook on medium heat for 10-15 minutes.
Remove from heat and spoon in enough peaches to fully cover the bottom layer of the Bundt pan.
With the second Pie Crust, layer over the the peaches base.
Add the peaches mix atop the second pie crust.
Flatten the balance of the dough with a rolling pin and slice into ½ inch x 6 inch rectangles.
Align a row of dough horizontally over the peach filling and another across vertically.
Sprinkle Cinnamon Sugar on the top to your liking.
Bake in preheated oven for 35 to 40 minutes or until crust is golden brown. Remove from oven and let stand 5 to 10 minutes before serving. Enjoy with vanilla ice cream or whipped cream!
I wanted to share an article I contributed to on the #PostFunnel forum managed by #Optimove. The site gathers some of the brightest minds in Performance Marketing and Customer Relationship Management to discuss hot topics and share information and ideas.
In this inaugural Advisory Partner series, we discuss key trends of the “New Normal” and share points-of-view from key partners at Data Culture and Dynamic Yield.
Enjoy the article and check back for our next installment of bright ideas and key topics!
I recently completed a Data Science course and wanted to review Principle Component Analysis (PCA) to develop my skills in understanding data variables highly correlated to the independent variable.
What is Principle Component Analysis (PCA)?
PCA is an ordination statistical method that reduces the dimensionality of multivariate data by creating a few new key explanatory variables called principal components (PCs). It’s mostly used as a tool in exploratory data analysis and for making predictive models.
For now, I’ll use PCA to evaluate the principal components of the Iris dataset. And later on I anticipate utilizing the methodology as part of a process of developing both a churn model and to predict customer lifetime value.
Iris Flower Data Set
The Iris Flower dataset is a multivariate set of 150 observations covering 3 flower species collected from various regions across four features; the width and length of the sepals and petals. It was initially introduced by the British statistician and biologist Ronald Fisher in 1936 and remains a very common practice set for data science (click here for more info). The data quantifies the morphologic variation of Iris flowers of three related species. Two of the three species were collected in the Gaspé Peninsula, all from the same pasture, and picked on the same day and measured at the same time by the same person with the same apparatus.
My analysis for this dataset will be to determine the key explanatory variables using PCA and the taking the following steps in R (click here to see R code):
Installing the caret package and library
Loading the Iris dataset
Transforming the data to normalize the variables using Log Scale
Running the PCA function and plotting the results
Analyzing the results
Here are 2 tables of output from running the PCA function in step 4 above:
Table 1 – Summary of Iris PCA Object
Table 2 – PCA Object Standard Deviation and Coefficients
Clearly shows PC1 explains 73% of coefficient variation, combined 95.99%
Sepal Length and both of Petal variables drive correlations
PC1 indicates most of where the correlation lies.
PC1 also explains variability and correlation to Sepal Length
The Setosa flower is clearly different from the Versicolor and Virginica which could possibly be explained by the pollination process or some other variable.
This is a great report from Mckinsey & Company on Analytics and competing in a data driven world. At the moment, location based data is realizing 50-60% of it’s value driven primarily by smart phone usage and applications while the following industries are all realizing less than 40%:
% Value Realized From Data & Analytics
Location Based Data ~ 50-60%
US Retail ~ 30-40%
Manufacturing ~ 20-30%
Public ~ 10-20%
With the exception of Location Based Data, many organizations across various industries have an uphill battle integrating and developing data driven cultures. (click link below to read full article).
Here are the Top 3 Reasons why companies aren’t yet realizing value from data and analytics:
Lack Of Analytical Talent
Siloed organizational structures
Plain Skepticism of the real value
Regarding the issue of analytical talent, I believe it’s available and sometimes even already exists in many companies but they are simply unsure of how to identify the key characteristics and/or channel it’s use. One thing I know for sure is that Google, Amazon and Facebook dominate their industries because of data and analytics and have access to the same talent pool as many other organizations.
Companies that are siloed will always have difficulty successfully implementing programs that require participation from multiple groups within the organization and stand permanently challenged at being successful as the competitive landscape changes and data becomes more widely utilized. And that I can say about skepticism is…..no risk, no reward!
Finally, here are 2 keys to success as identified by Mckinsey & Company:
a) Incorporate data & analytics into the company’s strategic vision
b) Develop business processes around infrastructure and talent
The above are simple, but powerful mechanisms to changing corporate culture around data and anlaytics. Once incorporated, companies can begin to seriously unlock the value of their data and perhaps create new channels and raise the competitive landscape.
I’m a freelancer and sometimes utilize the UPWORK platform for contract jobs. I recently experienced a scammer on the platform working to get my personal information to most likely steal my identity.
The perpetrator responded to my proposal and requested I communicate to his Gmail account. We connected on Google Hangouts and started a text conversation which was supposed to be a 15-20 interview. In the midst of job performance and character questions came a question about my bank name and subsequently a request for my full name, address, dob, email and phone number. This quickly raised my suspicions about his intentions and I ended the session and blocked his account.
Be extremely careful when dealing with people of the internet, even if they are offering a job. And more importantly, know what questions cannot be asked during an interview.
Here are 4 tips for avoiding scammers on the platform:
1. Communication – don’t respond or communicate outside of the platform. There’s video, chat and messaging built-in to the platform.
2. Personal Information – guard it with your life….no one needs your DOB, Email, Phone, Address and Bank Account to make payment. It’s as simple as using Paypal which requires on an Email.
3. Verify – confirm that Upwork has verified the employer and that they’ve done business before.
4. Report Them – be sure to alert the administrators if you feel someone is trying to scam or engage in fraudulent activity.
Internet scammers are extremely savvy and constantly look for ways to exploit innocent people. They explore victims on everything from Social, Emails and Dating sites to Freelance applications. Once they have gained your trust, they will ask you for money, gifts or your banking/credit card details. Always consider the possibility that the approach may be a scam, particularly if the warning signs listed above appear.
I recently came across and interesting infographic about Customer Churn on Website Magazine. It provides data on 2 like companies and indicates the financial impact of customer churn which can be devastating to business profitability and overall value (click image below for infographic)
In my experience this certainly holds true and companies should take note of this measure and work to understand how it may impact their business. I’ve calculated churn on many occasion and here’s my formula:
Churn = Beginning Customer Universe + New Customers + Reactivated Customers – Lapsed Customers
The above should be calculated every month at the very least so that any improvement can be tracked and historically compared. If run monthly, New Customers come on file over the past 30 days while customers lapse after 12 months and reactivate if a transaction occurs after not being see since over that same time-frame.
Next, here are my 3 tips to combat negative customer churn:
1) Implement CRM – hire/train a professional whose primary job is to maintain the customer relationship and create value in the process. Marketing Segmentation will be a key part of this strategy as it will help to identify customers likely to attrite and those preparing for the next step in their lifecycle.
2) Install and Execute Marketing Automation – digital and marketing automation will do wonders for customer churn by keeping the contact and communication constant with customers. This will help leverage your customer contacts and develop the feedback cycle so that business can better understand the customer’s willingness to interact and communicate.
3) Setup A Loyalty Program – Loyalty programs have known to heighten customer engagement and increase Lifetime Value (LTV). They can be as simple or complicated as business prefers, but should always take into account what the customer wants and needs.
The battle to decrease negative churn should be on every executive’s mind if they desire business relevance and longevity. The answers to improved churn will always lie in the data and tactics can be set with good Marketing and Loyalty programs.
Last year retailer Canada Goose decided to take business online in addition to it’s current wholesale and brick and mortar strategy. This is good news for them and probably not a moment too soon!
In the article (click here) the message is that they hope to close a 15-point margin gap between its rivals by selling online. This is all well and good, but here are some other points to consider:
> Shipping Costs – customers will be willing to pay a limited amount for shipping. This is one of the reasons Amazon’s model is so effective….low cost delivery! They’ll need to run plenty of testing in this area to see what works.
> Return Policy – do research in this area to understand the impact of potential returns on the business. Implement a good policy and watch closely as it could weigh on margin and feed negative publicity on social sites.
> Social Sharing – evaluate how this can be incorporated into the new website, if possible, so that existing customers “word of mouth” can help do the selling. In this process, and with the right tools, they could also find brand ambassadors to push the message in new markets.
> Analytics – some investment must go to both E-commerce and Consumer analytics so they can quickly see how the online customer differs from Wholesale and Brick and Mortar customers. There will without a doubt be distinctions in the products customers chose and their shopping habits, not to mention that there could be big demographic swings. Analytics will get them on top of these issues so adjustments can be made quickly.
> Automation – there’s usually not much marketing automation required in a brick and mortar or wholesale business. But for E-commerce, it can make a significant difference in conversion as they evaluate the sales funnel.
This an exciting time for Canada Goose as they bring their business forward and onto the digital landscape. Making sure that shipping and returns, analytics and automation and social are all given the proper review and investment will be the key to their success.
CRM Systems are becoming increasingly more important in today’s information and digital age. Companies rely on them to help provide the Who, What, When and Where of sales, marketing and communication. But without good and clean data, it would be challenging to for Marketers to find their answers and effectively drive engagement.
In my experience, roughly 15-20% of CRM Database records go bad each year. And here are the Top 5 reasons:
1) Movers – people have physically changed address and have not provided forwarding information.
2) Deceased – some will have lost their lives over the year.
3) New Accounts – some will have created new accounts with updated information.
4) Data Entry Errors – sales people, data entry clerks and even the customer can enter bad info.
5) Missing Data – some records will be partially entered at POS (Point-Of-Sale) for many reasons.
CRM users use the term “course data” to describe information that is broken and inaccurate. And we use the term “data scrubbing” as the process for evaluating data for inaccuracies and subsequently cleaning it. Companies that fail to identify course data and scrub it will find their marketing costs rising and campaign effectiveness dwindling.
A large mail house, CRM Vendor or your Technical team can help with data hygiene. And, it may actually require some input from all 3 in order to get the job done properly. As well, I wouldn’t be surprised if some Big Data companies also provide data scrubbing services (see data cleansing cycle below).
No matter who does the job, it’s important to be sure the cleansed data flows into connected systems such as Email, Ecommerce, SMS and the like…..where ever there are marketing touch-points. Failure to make this connection will cause the issues to be further perpetuated. I should also note that any address information should be standardized in the “cleansing” process so that it’s consistent with the USPS and ensure delivery.
Having bad data in your CRM and related systems can cause the following issues:
a) Undelivered Mail – each bad address in your mail file will cost you postage, printing and paper.
b) Reduced Engagement – missing name, suffix and or salutation information will reduce your ability to personalize marketing messages and thus grab the customer’s attention.
c) Erroneous Segmentation & Targeting – partial records can impact customer segmentation, especially if it causes multiple records to be created for a single customer. Sales can be scattered across multiple accounts.
All of the above issues will substantially impact customer engagement, conversion and campaign ROI. This can be a perpetual campaign if scrubbing systems aren’t setup and maintained on a regular basis.
Oftentimes company divisions are misaligned on Marketing Metrics which could put many of the customer driving programs in jeopardy. In fact, it is quite common for the Sales and Marketing departments to have very different ideas about performance metrics than the Finance department.
Marketing’s typical and key metrics are usually as follows:
ROAS – meaning return on Advertising Spend and calculated as Sales/Advertising Spend
Impressions – number of people exposed to advertising media
Click Thru Rate (CTR) – percentage of media viewers who clicked on media or Clicks/Ad Sends
Conversion Rate (CVR) – percentage of media recipients who placed an order or Orders/Clicks
Lift – meaning sales or conversion rate above the standard or control group
Cost Per Impression (CPI) – media cost per impression or ad displayed
Cost Per (M) Impression (CPM) – media cost per one thousand impressions or ads displayed
Cost Per Acquisition (CPA) – media costs per customer acquired
Cost Per Click (CPC) – media costs paid for each click on the advertisement
What becomes clear and predominate in Marketing Metrics is a measure of Cost. Most often Marketers are beholden to a limited budget on which to drive traffic and sales. And the key to managing the budget is to be sure spend is generally customary and reasonable to acquire each new customer or order.
On the other hand, Finance and Accounting departments are primarily concerned with Sales growth and Return on Investment (ROI) as they are charged with being the custodian for all money spent in the company to earn sales.
Finance – Higher Returns VS Marketing – Low Acquisition Cost
This difference in objective I call “The Great Divide” as it can polarize the company where these departments are concerned. With all marketing metrics being considered, if Finance cannot translate sales driven by marketing spend to ROI, it may jeopardize any further spend on those activities and oftentimes can lead to cutbacks.
So what does it take to bridge the “The Great Divide”? Incrementality! Amongst other measures, Marketers must endeavor to calculate the incremental components of their efforts. Whether it be sales, conversion, orders or customers it must be understood how $1 of marketing spend delivers on any other unit of measure which is the basis for ROI analysis. This would help develop a much cleaner path to understanding for Finance and help support Marketing’s position on spend and budgets.