Tag: Analysis

Complete guide to retail analysis

In 2019, the pace and scale of retail has never been greater.

With more and more D2C brands being created every week in an attempt to capture a share of the market in your space, have you ever wondered if they are all growing so fast?

Your initial response might be "customer service" or "venture capital dollars," but this answer is incomplete.

Do you want to know the answer?
The first order of operations (after verifying that you own a product or brand that buyers buy into) is to put in place the analytical infrastructure to start measuring differentiated initiatives.

  • Nobody recruits 100 representatives of customer success without first understanding the cost structure and the expected increase in the value of the customer's lifetime.
  • No venture capital is willing to invest continuously in activities that do not achieve the expected return on the dollars spent.

Breakdown of this guide
Now that you have a rough idea of ​​the value of the retail analysis, break down the sections (feel free to jump to the most relevant sections):

  • Chapter 1: What is the analysis of detail?
  • Chapter 2: What work can analysis help retailers solve?
  • Chapter 3: Data Needed to Get Started
  • Chapter 4: Difference between Executive, Management, and Operational Dashboards
  • Chapter 5: 203 Actions for Retailers and Trademarks
  • Chapter 6: 9 measures for start-ups and small businesses should focus on
  • Chapter 7: Strengths of Operational Analysis
  • Chapter 8: Leveraging Analyzes to Ensure Long-Term Success

What is the analysis of detail?
Retail analysis involves using data to measure performance, improve decisions, and monitor results. In retail, common applications include: measuring the performance of marketing campaigns, monitoring the supply chain, inventory management and merchandising.

The current end result of the analyzes is the monitoring and monitoring of metrics and key performance indicators (KPIs), which are then used as proxies to measure the health of the enterprise.

Where do the data come from?
Given the proliferation of current technologies, data can come from many sources. Some include:

  • Daily transactions
  • Transactions
  • Daily operations
  • Buyer loyalty initiatives
  • Product Information
  • Marketing Attribution Tools
  • External data sets (eg, weather)

New technologies have enabled us to increase our analytical prowess by combining data sets (for example, the effectiveness of promotions from Marketing Attribution and sales data) to provide a more complete picture before making a decision.

With the proliferation of sensory technologies (for example, IoT devices) and the reduction of storage and computing costs in cloud technologies, it is now easier than ever to collect, store and analyze data.

The post goal, however, has not changed.
What should be my next action to help improve my business by helping to increase sales or reduce costs?

What analysis can help retailers solve?
TLDR: NOT everything.

You were already doing the tasks to be done before implementing the analysis. Analytics is designed to help you measure, analyze, and focus your efforts.

Example 1: Merchandising Planning

  • Before the analysis:
  • "Guess" about the product that would do well.
  • (If you had more than one sales channel, it would be too complex to handle the differences)
  • After the analysis:
  • To provide a more global estimate, use;
  • historical data,
  • product attributes,
  • price elasticity,
  • traffic forecast,
  • customer acquisition cost and
  • marketing budgets

Example 2: Rationalization of customer engagement

  • Before the analysis:
  • Send marketing emails to every 1,000,000 buyers on the list of newsletter subscribers until something happens
  • After the analysis:
  • 1st: List of segments based on opening rates (discard ones that are never open)
  • 2nd: List of segments based on the purchase history
  • 3rd: use the product affinity analysis to see which products tend to be bought together
  • 4th: send emails according to different purchase history

Example 3: Optimize Digital Marketing Expense

  • Before the analysis:
  • I know that 50% of my marketing budget is wasted. I just do not know what 50%.
  • After the analysis:
  • 1st: Monitor the cost of customer acquisition (CAC) on all advertising channels
  • 2nd: zoom in on the Google Analytics multi-channel funnel
  • 3rd: Reallocate the budget from useless channels to those who work

Two buckets
Running a business is difficult.

We do not always have time to look for the next great analytical tool. Therefore, when it comes time to invest in one, it usually falls into one or two compartments;

1. Business is incredibly good, it's time to optimize
The car seems to be working, it's time to invest wisely in the engine.

How can I convert $ 1 into $ 5?
SEMrush

Common questions at this stage include:

  • How to replicate this success with another product?
  • How to replicate this success with new buyers?
  • How to optimize my marketing funnel to attract new buyers?
  • What should I do to increase the lifetime value of my loyal customers?

2. Business is bad, what should I do?
The car seems out of order, why?

How can I turn the bleeding rod?

Common questions at this stage include:

Invest early
If you fall in the second bucket, I'm sorry to say, but it may be too late.

Running a business is difficult.

Why not spend a little more time early in the analysis so you do not ruin everything later?

Here is the reality:

  • If you have not collected any data, follow through NOTHING and MONITOR NOTHING.

You have no way to improve your business because you have no idea what was going on (other than that there was a fluctuation in sales during seasonal periods)

Assuming you have invested early. What can you expect from the analysis tools?
1. Increase decision making in the business
There should be less guesswork in everyday affairs.

With more data points on products, buyers and sales, you SHOULD better understand what initiatives need to be removed and in which it is worth investing.

With the same data in front of all team members, EVERYONE should be able to make the same decision (almost) of time without you having to intervene in every decision.

2. Monitor metrics and report KPIs
Gather a holistic dashboard that can be used to organize and monitor information about everything that's going on.

Some basic goals that deserve to be monitored include …

  • Sales rate
  • Promotional performance
  • Basket compositions
  • Pricing effects
  • Return on marketing investments

3. Optimize opportunities
Some examples…

  • Funnel analysis to improve the number of completed transactions
  • Improve targeting of social media ad sets to reduce acquisition costs
  • Promotional mechanisms to improve the average value of orders
  • Purchase Bulletin to increase the value for life
  • Birthday vouchers to increase the frequency of purchases

Read it.
If you omitted everything else, focus on this last section.

How to start?
1. Write down the functional areas of your business. Some examples

  • Customer service
  • Digital marketing
  • Merchandising
  • Inventory Management.

2. If these areas were 10 times better than today. What would it look like?

  • Mean time of first response lower
  • Lower cost of customer acquisition
  • Higher average margins
  • Higher sales rates

3. These are your settings. Measure everything that you think can help improve these numbers. Find tools that help you improve these numbers !!!
Read the next section on data requirements.

Looking for more? Continue reading here
If you need help with detail analysis, do not hesitate to contact here.

Functional Analysis – In a mixed venture composed of Italians, Turks, Germans, Chinese and Australians

In a mixed venture composed of Italians, Turks, Chinese, Germans and Australians:
1. Italians represent one third of the number of Germans and three less than the Australian.
2. The Chinese and Germans are three times more numerous than the Chinese and the Australians.
3. Chinese and Australians make up less than half of the group, while Chinese and Australians make up 7/16 of society.
How many people of each nationality are there?

Data analysis task help

This blog is one of the most important blogs I have read today. I like it. if you need help with a data analysis task or if you need the help of our experts in memory data analysis, contact us now. You will like our services.

Classical analysis and odes – Vasicek model and sports interest rate parameterized by reversion rate

In solving an SDE, I want to get the analytical results for the mean and variance of the extended Vasicek model process

$ dr = (η-γr) dt + cdX $

while $ γ $ is the reversion rate and $ s = η / γ $ the average short rate.

How can I put $ X (t) = r (t) – s $ and solve by integrating both sides of the SDE with the help of the factor of integration $ e ^ {yt} $ and in a second step, derive the mean and the variance?

calculation and analysis – How to find and plot the intersection of these three surfaces?

I know how to find the intersection region of these cylinders using integration, but how could I plot this in mathematica? That's what I've

ContourPlot3D[{x^2 + y^2 == 1, x^2 + z^2 == 1, 
  y^2 + z^2 == 1}, {x, -3, 3}, {y, -3, 3}, {z, -3, 3}, 
 AxesLabel -> Automatic, PlotLegends -> "Expressions"]

That's what I've got up to here

What is a content analysis report in Google Analytics? – SEO help (general discussion)

Consider the detail of the content as a high level view of the organization of your content. You can use this report to see if your site feed is confusing users when they visit the landing page. For example, you can see where all of your audience responds better to page A and not page B which deals with the same subject.

complex analysis – Prove a generalized theorem of Goursat

I read Steins and Shakarchi Complex analysis and I am looking at Exercise 6 of Chapter 2. He says:

Let $ Omega $ to be an open subset of $ mathbb {C} $ and let $ T subset Omega $ to be a triangle whose inside is also contained in $ Omega $. assume $ f $ is holomorphic in $ Omega $ except possibly in one point $ w $ in the interior $ T $. Prove that if $ f $ is bounded by $ w $then $$ int_T f (z) , dz = 0 $$

I am aware of this question but I do not manage to analyze the geometric construction mentioned in the comments. I imagine that the first step probably consists of two subdivisions $ T $ in three new triangles $ T_1, T_2, T_3 $ by connecting each vertex of $ T $ at $ w $ and observing that $$ int_T f , dz = int_ {T_1} f + int_ {T_2} f + int_ {T_3} f $$ since we should have a cancellation. That's where it gets a little cloudy for me:

(1.) Is this annulment even justifiable since we have no information about what is happening at $ w $? How can we be sure that the integral is well defined if we integrate on a point that is potentially a singularity?

(2.) Assuming that this is correct, how can we show that each of the three integrals is equal to zero?

Keeping in mind that it is an introductory manual, I would therefore like my solution to remain relatively untechnical.

From the question that I've linked, it seems the way forward is to define an auxiliary contour type and look for a type of boundary, but I'm not sure of the details.

SQL Server – How to improve a simple update statement that causes blocking issues, unnecessary index analysis in the query plan

Please, consider me – I'm on the development side but I was tasked with finding the cause of several blocking issues after upgrading from SQL Server 2008 to 2016. Blocking is intermittent everything goes well for a few weeks, then maybe once. or twice a month, the application becomes unusable. Here is one of the recurrent queries incriminated when this happens: 

UPDATE campaign_log
SET date_modified = No 2019-06-18 14:28:19 & # 39;
, mail_disposition_status = & # 39; 1 & # 39;
, status_date = N / 06 / 17/2019 & # 39;
, activity_date = N / 05 / 22/2019 & # 39;
O id = N 03938240-0112-437C-AF97-ECCF2EF78E77 & # 39;

It seems that this https://www.brentozar.com/pastetheplan/?id=H1YyzoqJS query plan tells me that the index scan [SUGARCRM].[dbo].[campaign_log].[idx_mail_disposition_status] is really inefficient.

I do not know why the column Mail_disposition_status is indexed or why this index is chosen to execute this query. Looks like he should maybe use an index on id? There are 2,622,020 rows in the campaign_log table.

Can any one indicate in the right direction what I should look for to solve this problem? Honestly, I'm pretty lost on what I'm looking for.

design – Data Analysis Flow

I write an application that will read the data, analyze it and then publish it.

Here is the flow that I created:

DB -> APPLICATION -> SPARK -> APPLICATION -> ELASTICSEARCH / EVENING -> UI

Below, I go to each step:

  1. DB – loading data from an external CSV file.
  2. APPLICATION – Loading data from the database, integrating into the template and sending a list of templates to Spark.
  3. SPARK – manipulate, filter and analyze
  4. APPLICATION – receive the results of SPARK, send the results to ELASTIC / SOIR
  5. ELASTIC / SOIR – indexing data.
  6. UI – display

Is this a typical way of managing such a system?

I'm pretty new to big data architecture, so thank you in advance for your suggestions.

real analysis – Resolution for a monotonic function – contraction operator for functions?

I want to solve a problem for a increasing function $ g (x) $, for $ x in [0,1]$ and with $ g (0) = $ 0 and $ g (1) = $ 1.
The solution will be the solution to the following equation
$ forall x $, $ f_1 (x) = f_2 (g (x)) $.
(with $ f_1 $ and $ f_2 $ known)

however, $ f_2 $ may not be invertible (it is, however, monotonous in pieces). So for some people $ x $, you have multiple solution $ g (x) $, which we will note $ (g ^ 1 (x), g ^ 2 (x), …, g ^ k (x)) $ (Given the structure of the problem, you still have a countable number of solutions).

However, overall, there is a unique solution growing $ g ^ * () $ of $[0,1] right arrow [0,1]$.
My problem is, I do not know how impose the constraint of monotony when solving a system, so I should be able to pinpoint the unique solution "directly" (I do not know if this is even possible).
Or in other words, I do not know the tricks to write the system with the integrated monotony, solve for the full function $ g () $, instead of solving by points $ x $ by $ x $.

I was trying to build a contraction mapping operator (maybe that's not the way to go), but I have to integrate the monotonicity constraint in the contraction operator and I do not know how to do that. (Because taken to a given $ x $, our system can give several solutions and therefore no contraction mapping is feasible).

PS: Just to be clear, in this particular case, I know how to encircle $ g () $ exploiting piecewise monotony entirely (and thus building piece-wise invertibility). My question is actually about tips for including monotony in a system (as an additional equation? Constraint?) And, if possible, using a contraction mapping with it.