Tag Archives: Analysis
Are my people working when the work needs to be done? Headcount Analysis with Power BI
One of the nice things about the Power Suite (the term I generally use to refer to Power BI, Power Pivot, and Power Query; does anyone else have any good terms?) is that it can easily allow you to replicate things that other systems of yours should do, even when those systems don’t work well. As an example, I had a manager come to me with a headcount analysis question that seemed like it ought to be straightforward, but our payroll system couldn’t do it: How many people do I have scheduled to work on a given hour of a given day? Are there any gaps or dips? And then the big one: How does this scheduling compare to the work my department has to do?
The manager had been trying to do just the headcount bit in Excel, and it worked okay for one hour at a time but was a serious pain to get a good overall picture. Comparing the headcount to work needed was a pipe dream at this point. So, I start working through the scenario and found out one of the main problems is this department has people working one shift on one day, and a different shift on another day. So, Employee 3 might be working 8 AM to 5 PM on Monday, but 4 PM to 8 PM another day, and so on and so forth.
I got similar requests from a few different managers for different purposes, so I dug around online for a bit and found…nothing. My Google-Fu had utterly failed me, and I couldn’t find anyone doing anything like this at the hour level (if anyone has a different way to do this, I’ll be watching the comments!). I let the ideas percolate around for a bit (read: five months) and tried a few approaches, and eventually came up with a solution. I don’t want to admit how many hours it took me, but like many things, once I figured it out I was amazed at how straightforward it was.
Part I: Headcount
The trick to this is realizing that you need an index. Since we’re at the hour level, you need a unique identifier for every hour of every day, or your schedules will be hopelessly snarled. And since the shifts can vary so much, there was no way to get around it but have one row for every hour of every day. I creatively named this the “Day/Hour Index,” because I’m terrible at naming things.
The columns highlighted in green are what I gave the manager to fill out. Every hour that someone was working had a 1, and I gave it to them with 0’s in everything (note that lunch breaks are 0’s, they aren’t working then so other coverage is needed!). Not the prettiest thing in the world, but the pretty comes later!
There’s also a really basic table showing what supervisors have what agents, although not all the agents have supervisors (for “reasons,” which I decided was outside my mandate on this one).
I’m not going to go through all the steps for pulling it in (though I will provide the data and PBIX at the end for those interested), but the main thing is to Unpivot the schedules, so you go from this:
To this:
You also need to set up a variant of a calendar table that has your day/hour index. I did this by the simple expedient of pulling in the table that had my schedules and removing the employee info:
Set up a basic table linking agents to their supervisors and you get all the data you need to produce this:
You can click on the bars for supervisors, days, hours, etc. and have everything else re-orient. You can search for an agent and see when they’re working. And at a quick glance, you see that 7 AM is a big dip.
Part II: Headcount vs. Work
The trick of this is how you define when work needs to be done. That’s a huge process question, and likely won’t be 100% answerable, so I recommend a couple of simplifying assumptions, examples we used are below:
- Figure out what you have data for, and work from that.
- Assume that work should be started as soon as it becomes available.
Assuming you have something resembling good data, all you need to do to get this to match up is add in a day/hour index associated with whatever date/time you’ve decided to use. The M for that is below Note that the steps are not sequential, I was doing this in the midst of other changes to clean my data up, and also note that we use a day system with Monday as 1 and Sunday as 7:
= Table.AddColumn(#”Changed Type”, “Start Time Hour”, each Time.Hour([Start Time]), Int64.Type)
= Table.AddColumn(#”Reordered Columns”, “Day of Week”, each Date.DayOfWeek([Start Time]), Int64.Type)
= Table.AddColumn(#”Replaced Value”, “Day/Hour Index”, each 1+[Start Time Hour]+([Day of Week]-1)*24)
Once you have your index, relate the table to your Day/Hour calendar table equivalent, and you can get your information (in my case, non-compliance):
And then the holy grail, combining it all together!
See how the Tuesday morning hours jump out at you as having work that needs to be done and no one to do it? That’s when you know you’ve done a good analysis! When the information politely indicates where, exactly, your problem is, and kindly asks for you to fix it so it can go back to looking pretty.
You can download the .zip containing the .pbix and .xlsx to follow along here.
No one knows the Power BI ecosystem better than the folks who started the whole thing – us.
Let us guide your organization through the change required to become a data-oriented culture while squeezing every last drop* of value out of your Microsoft platform investment.
Analysis Services is 20 years old!
It’s amazing to think that Analysis Services is 20 years old. This product defined BI as we know it today. A historic milestone for a workhorse of industry. Here is the original press release.
We created a webinar to celebrate. It includes a timeline of the history, community tools, and interviews from some of the influencers of Analysis Services over the years. Some of the interviews have hilarious moments. We even managed to get an installation of Analysis Services 2000 for a “then and now” demo. Oh, and of course we included some OLAP jokes.
Here are the interviewees:
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Chris Webb
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Darren Gosbell
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Jeffrey Wang
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Josh Caplan
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Kasper de Jonge
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Marco Russo
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Will Thompson
Which interview did you find the funniest? What is your favorite Analysis Services feature? What are you most excited about the future of Analysis Services?
Special thanks to Peter Myers, Adam Saxton and Charles Sterling for helping make this happen. Thanks also to all those who participated in the Analysis Services community over the years!
Happy 20th birthday Analysis Services!
Configure Text Analysis in Dynamics 365 for Sales/Customer Engagement through Azure Machine Learning Text Analytics
Azure Machine Learning has continually improved and developed, and Microsoft has consistently ensured full integration with Microsoft Dynamics 365. This provides tremendous benefits to all types of business. Specifically, it provides text analysis capabilities for case attributes, identifying the most effective KB articles. The following will help you configure Dynamics 365 for Sales/Customer Engagement (CRM) text analysis with Azure Text Analytics Service.
Add Azure Text Analytics Service in Azure
- Add Text Analytics to Azure.
- Copy Endpoint and Key as in the images below.
Configure Azure Machine Learning Text Analytics
After configuring Text Analytics in Azure, Machine Learning Text Analytics can be configured in Dynamics as follows.
- Enable the Text Analytics service. NOTE: This is part of the preview. Go to Settings > System Settings:
- Configure Text Analytics. Go to Settings > Administrator:
Text Analytics service in Dynamics has now been configured. It’s now capable of suggesting similar appropriate cases or KB Articles. The following demonstrates how to use this for KB article suggestions.
Use Text Analytics to suggest KB Article search on Case
- Configure KB Article Search: Settings > Service Management > Knowledge Search Field Setting, and then click New:
- Configure KB Article search on Case Form. Open a case form for customization and change properties in Activities Tab Properties:
Behavior can be reviewed once this is completed.
Text Analytics at Work
In the example, the same case is used but the Maximum number of key phrases in Knowledge Search Model has been modified. Initially, it was set to 2; it is now set at 10.
Maximum number of key phrases = 2
Maximum number of key phrases = 10
For questions or more information about working with Microsoft Dynamics 365, please.
5 techniques to take your data analysis to another level
Implementing a business intelligence suite in your organization is about more than simply collecting additional data—it’s about converting this data into actionable insights. The amount of data an organization can collect today from a variety of sources offers it the ability to see under the hood, understand which processes are working, and help teams prepare for future trends. However, without properly analyzing and comprehending the data you collect, all you have is figures and numbers with no context.
More importantly, there isn’t one right way to analyze data. Depending on your needs and the type of data you collect, the right data analysis methods will shift. This also makes it necessary to understand each type of data, and which methodology can deliver the best results. Even so, there are some common techniques that come included in most data analytics software because they’re effective. These five data analysis methods can help you create more valuable and actionable insights.
Quantitative and Qualitative Data—What’s the Difference?
The first step in choosing the right data analysis technique for your data set begins with understanding what type of data it is—quantitative or qualitative. As the name implies, quantitative data deals with quantities and hard numbers. This data includes sales numbers, marketing data such as click-through rates, payroll data, revenues, and other data that can be counted and measured objectively.
Qualitative data is slightly harder to pin down as it pertains to aspects of an organization that are more interpretive and subjective. This includes information taken from customer surveys, interviews with employees, and generally refers to qualities over quantities. As such, the analysis methods used are less structured than quantitative techniques.
Measuring Quantitative Data
Quantitative analysis methods rely on the ability to accurately count and interpret data based on hard facts. Our first three methods for upping your analysis game will focus on quantitative data:
1. Regression Analysis
Regression studies are excellent tools when you need to make predictions and forecast future trends. Regressions measure the relationship between a dependent variable (what you want to measure) and an independent variable (the data you use to predict the dependent variable). While you can only have one dependent variable, you can have a nearly limitless number of independent ones. Regressions also help you uncover areas in your operations that can be optimized by highlighting trends and relationships between factors.
2. Hypothesis Testing
Also known as “T Testing”, this analysis method lets you compare the data you have against hypotheses and assumptions you’ve made about your operations. It also helps you forecast how decisions you could make will affect your organization. T Testing lets you compare two variables to find a correlation and base decisions on the findings. For instance, you may assume that more hours of work are equivalent to higher productivity. Before implementing longer work hours, it’s important to ensure there’s a real connection to avoid an unpopular policy.
3. Monte Carlo Simulation
As one of the most popular ways to calculate the effect of unpredictable variables on a specific factor, Monte Carlo simulations use probability modeling to help predict risk and uncertainty. To test a hypothesis or scenario, a Monte Carlo simulation will use random numbers and data to stage a variety of possible outcomes to any situation based on any results. This is an incredibly useful tool across a variety of fields including project management, finance, engineering, logistics, and more. By testing a variety of possibilities, you can understand how random variables could affect your plans and projects.
Measuring Qualitative Data
Unlike quantitative data, qualitative information requires moving away from pure statistics and toward more subjective approaches. Yet, you can still extract useful data by employing different data analysis techniques depending on your demands. Our final two techniques focus on qualitative data:
4. Content Analysis
This method helps to understand the overall themes that emerge in qualitative data. Using techniques like color coding specific themes and ideas helps parse textual data to find the most common threads. Content analyses can work well when dealing with data such as user feedback, interview data, open-ended surveys, and more. This can help identify the most important areas to focus on for improvement.
5. Narrative Analysis
This kind of analysis focuses on the way stories and ideas are communicated throughout a company and can help you better understand the organizational culture. This might include interpreting how employees feel about their jobs, how customers perceive an organization, and how operational processes are viewed. It can be useful when contemplating changes to corporate culture or planning new marketing strategies.
There is no gold standard for statistical analysis or right way to do it. The method you choose should always reflect the data you’ve collected, and the type of insights you want to extract. Matching the right data and analysis helps uncover better insights to optimize your organization.
7/19/18 Webinar: Next Generation Location and Data Analysis using Mapbox and Power BI
Join Charles Sterling and Sam Gehret as they walk through how Mapbox and Power BI can use location data to tell your story using next generation maps.
When: 7/19/18 10AM PST
If you are not familiar with Mapbox, it is the location data platform for mobile and web applications. They provide building blocks to add location features like maps, search, and navigation into any experience you create.
Sam Gehret is a Solutions Engineer for BI and Data Viz at Mapbox. He currently manages the development and roadmap for the Mapbox custom visual for Power BI. Sam has over 7 years of Business Intelligence experience working in both product and sales at another large BI company. He holds a BA from Dartmouth College and is a graduate of the General Assembly Javascript bootcamp.
7/12 Webinar: Text Analysis in Power BI with Cognitive services with Leila Etaati
This week we will be delivering a sneak peak of some of the Business Applications Summit content!:
Text Analysis in Power BI with Cognitive services with Leila Etaati
Data that we collected always is not about numbers and structured data. In any organization, there is a need to analyze the text data such as customer comments, extract the primary purpose of a call from its scripts, detect the language of customer feedback and translate it and so forth. To address this issue, Microsoft Cognitive Services provides a set of APIs, SDKs, and services available to developers to do text analysis without writing R or Python codes. In this session, I will explain what is text analysis such as sentiment analysis, key phrase extraction, Language detection and so forth. Next, the process of text analysis in Power BI using cognitive services will be demonstrated.
When: July 12, 2018 10:00 AM PST
Where: https://www.youtube.com/watch?v=WWod8ETS7J8
About Leila Etaati
Leila is Data Scientist, PhD, MVP, and BI Consultant, and Speaker. She has over 10 years’ experience working with databases and software systems. She was involved in many large-scale projects for big sized companies. Leila has PhD of Information System department, University of Auckland, MS and BS in computer science.
She worked in Industries including banking financial, power and utility, manufacturing … She is a lecturer and trainer in Business intelligence and data base design course in University of Auckland.
Leila speaks in international SQL Server and BI conferences such as Microsoft Ignite, PASS Summit, PASS Business Analytics, PASS Rally, and many SQL Saturdays in USA, Europe, Australia, and New Zealand on Machine Learning and Analytics topics.
Ask Leila about these technologies and areas;
- Machine Learning
- Advanced Analytics
- Predictive, Prescriptive, and Descriptive Analytics
- Azure Machine Learning
- R
- Python
- On-Prem Machine Learning with SQL Server, R and Python
- Power BI and R
- Power BI
- Stunning Visualization with R
- Microsoft BI Suite in SQL Server
- Azure Stream Analytics
- IOT
- Real-time Streaming
When Leila is not doing data related work, his hobbies are;
- Playing Game Boards!
- Watching all type of movies!
- Swimming if it is not too cold!
IBM's Watson Enlisted for B2B Sentiment Analysis
B2B customer support firm
TeamSupport on Tuesday announced the addition of sentiment analysis technology, powered by IBM’s Watson, to its customer relationship platform.
The new tech provides automatic text analysis of email or chat responses to assess how a customer feels, so customer support teams can prioritize and personalize their outreach efforts. It includes predefined categories such as “satisfied” and “frustrated.”
The sentiment analysis capability is an addition to TeamSupport’s Customer Distress Index, which analyzes ticket and response data to categorize levels of customer satisfaction at a company level.
IBM Watson data will be used to score response data at both the ticket and overall customer level to help inform customer strategy and provide a framework for customer success, TeamSupport said.
The company also plans to use data from IBM Watson to expand other areas of its service, noted TeamSupport CEO Robert Johnson.
Customer Support Table Stakes
“Sentiment analysis for chat and text has been around for a long time,” observed Rebecca Wettemann, VP of research at Nucleus Research.
“RightNow had it before the Oracle acquisition. IBM had it long before Watson was its marketing albatross,” she told CRM Buyer.
Sentiment analysis “has evolved to be more sophisticated and cost-effective, and there are varying levels of effectiveness of such solutions,” Wettemann pointed out. “It’s really table stakes by now.”
The technology’s long history in customer support has revealed many uses, noted Ray Wang, principal analyst at Constellation Research.
“Many pioneers of this technology have shown how it could — in customer support — change prioritization of issues [and] response time to cases,” he told CRM Buyer.
More importantly, it enables reps to know the customer interaction history before responding, Wang said.
Outdoing IBM
What is interesting about TeamSupport’s approach is that it “has made the investment to operationalize Watson in a way IBM has — so far — been unable to do,” Nucleus’ Wettemann noted. “This may make it accessible to smaller teams without the resources to build out their own sentiment model.”
However, “there are plenty of in-app options available at multiple price points today,” she remarked.
Other companies that offer sentiment analysis to help customer support efforts include Medallia and Clarabridge, Wang said, “but they have traditionally been more focused on B2C than B2B.”
Help Desk on Steroids
TeamSupport’s tools were designed from the ground up by B2B support professionals for organizations providing external customer support. The company compares its offerings to help desk software from Zendesk, Freshdesk, Service Cloud, Parature, Intercom, Kayako and HappyFox.
Customers include the American Lung Association, the United States National Basketball Association, Agilent Technologies and Comcast.
“If the training programs for TeamSupport improve the accuracy of existing capabilities in the market, they have a good shot at gaining market share,” Wang observed.
Resolving the Growing B2B Buyer-Seller Divide
There’s a growing
divide between B2B buyers and sellers, according to the Miller Heiman Group, with buyers seeing sales teams as product representatives and engaging them later in the purchasing process.
Sentiment analysis may help breach that divide.
For example, Insights, a feedback analysis solution Yotpo announced at Shoptalk in March, uses natural language processing (NLP), artificial intelligence and machine learning technology to analyze reviews and their related opinions.
Retail brands can use Insights to analyze customer feedback at scale to identify trends or untapped markets, receive alerts about new issues, and gain instant feedback on new product releases.
Sentiment analysis “uses NLP to identify, prioritize and suggest responses to de-escalate customer issues,” said Constellations’ Wang, “and, more importantly, proactively resolve issues before they become a problem.”
Richard Adhikari has been an ECT News Network reporter since 2008. His areas of focus include cybersecurity, mobile technologies, CRM, databases, software development, mainframe and mid-range computing, and application development. He has written and edited for numerous publications, including Information Week and Computerworld. He is the author of two books on client/server technology.
Email Richard.
New memory options for Analysis Services
We’re happy to introduce some new memory settings for Azure Analysis Services and SQL Server Analysis Services tabular models. These new settings are primarily for resource governance, and in some cases can speed up data refresh.
IsAvailableInMdx
The IsAvailableInMdx column property is available in Azure Analysis Services and SQL Server Analysis Services 2017 CU7.
It prevents building attribute hierarchies, reducing memory consumption. This means the column is not available for group-by queries to MDX clients like Excel. Fact (transactional) table columns often don’t need to be grouped and are often the ones that use up the most memory.
This property can also improve performance of refresh operations; particularly for tables with lots of partitions. Building attribute hierarchies can be very expensive because they’re built over all the data in the table.
Currently, this property is not yet exposed in SSDT. It must be set in the JSON-based metadata by using Tabular Model Scripting Language (TMSL) or the Tabular Object Model (TOM). This property is specified as a Boolean.
The following snippet of JSON-based metadata from the Model.bim file disables attribute hierarchies for the Sales Amount column:
{
"name": "Sales Amount",
"dataType": "decimal",
"sourceColumn": "SalesAmount",
"isAvailableInMdx": false
}
QueryMemoryLimit
The Memory\QueryMemoryLimit property can be used to limit memory spools built by DAX queries submitted to the model. Currently, this property is only available in Azure Analysis Services.
Changing this property can be useful in controlling expensive queries that result in significant materialization. If the spooled memory for a query hits the limit, the query is cancelled and an error is returned, reducing the impact on other concurrent users of the system. Currently, MDX queries are not affected. It does not account for other general memory allocations used by the query.
The settable value of 1 to 100 is a percentage. Above that, it’s in bytes. The default value of 0 means not specified and no limit is applied.
You can set this property by using the latest version of SQL Server Management Studio (SSMS), in the Server Properties dialog box. See the Server Memory Properties article for more information.
DbpropMsmdRequestMemoryLimit
The DbpropMsmdRequestMemoryLimit XMLA property can be used to override the Memory\QueryMemoryLimit server property value for a connection. Currently, this property is only available in Azure Analysis Services. The unit of measure is in kilobytes. See the Connection String Properties article for more information.
RowsetSerializationLimit
The OLAP\Query\RowsetSerializationLimit server property limits the number of rows returned in a rowset to clients. Currently, this property is only available in Azure Analysis Services.
This property, set in the Server Properties dialog box in the latest version of SSMS, applies to both DAX and MDX. It can be used to protect server resources from extensive data export usage. Queries submitted to the server that would exceed the limit are cancelled and an error is returned. The default value is -1, meaning no limit is applied.
In-Depth Cash Flow Analysis and Forecasting Webinar
Having enough cash flow to keep your business going is a primary focus for a business owner who wants a successful business. That’s why we developed a series that will provide you with all the accounting tools you’ll need to run a successful business. In this webinar, the following topics will be covered.
-In-Depth Cash Flow Analysis
-Cash-Flow Forecasting
Time: 12:00PM EST – 12:30PM EST
Date: Thursday May 24, 2018
How to Perform a Competitive Analysis of Your Brand
Let’s find out how to perform a competitive analysis – and how you can use that information to your advantage.
Why do you need a competitive analysis? Building a successful marketing plan requires knowing your customers and their pain points intimately. That is step one. And step two is knowing how your competitors are addressing their needs (if they are) and where opportunities may exist for your company.
Marketing ethics 101: Don’t be shady!
Before I go any further, I want to state something up front. This post is meant to teach you how and why to do a competitive analysis. It’s not meant to encourage or even hint at something more malicious. Stealing – whether from your competitors or your neighbors – is never cool. Please don’t do it!
What is a competitive analysis?
Now that we have that out of the way, let’s get into the topic at hand: competitive analysis. What is a competitive analysis?
A competitive analysis is a formal process of evaluating what your competitors are up to. And in this case, we’re talking about a qualitative analysis, not a quantitative or cost analysis. You’d look at things like their product, their marketing approach, their customers, their strengths and weaknesses – and how those may become threats. This may sound similar to a SWOT exercise – and that is for a reason. SWOT (which stands for Strengths, Weaknesses, Opportunities, and Threats) and Competitive Analyses are related concepts. They’re marketing cousins, so to speak.
Why: the benefits of doing a competitive analysis
With a successful competitive analysis, you look at what the other guys are doing, and, more importantly, what you can do in response. A competitive analysis pulls you out of your myopic bubble to find what else is going on in the world … to take a step back and then analyze your findings.
Here are some other benefits of doing a competitive analysis:
- Get a view of the market landscape. See what else is out there – and where customers may go if they don’t choose you. You or your predecessor probably did this when your company or product was initially concepted. This isn’t a one-and-done exercise, though. You need to constantly keep an eye on the competitive landscape.
- Meet a need. Ultimately your goal as a marketer (or R&D team member) is to find a niche (or chasm) to fill a need that isn’t already being met. To create a product or service that customers want or need – and expertly sell it in a way that garners the most traffic. Essentials of Marketing, a textbook on all things marketing that sits on my bookshelf, words it this way: “The search for a breakthrough opportunity – or some sort of competitive advantage – requires an understanding not only of customers but also of competitors.”
- Find ways to get new customers – or hang on to the ones you have. During a competitive analysis, you get time to think about acquisition and retention. For example, how are you competitors winning new customers or retaining them? Look at their loyalty programs and win-back strategies. Also consider what you could do to win customers from your competitors and take over more market share.
When to perform a competitive analysis
There are a variety of times to perform a competitive analysis. Maybe you’re launching a new product and need to know how to approach it. Maybe your brand is feeling stale and you need ideas. Or, maybe it’s simply a downtime in your marketing schedule and you want to do extra credit work. Whatever the case, I encourage you to perform competitive analyses at least once or twice per year.
How to perform a competitive analysis
Now let’s get into the how-tos.
- First, scope out the competition and name them. In order to perform a competitive analysis, you have to know who your competitors are. If you have a list, great. Go grab it. If not – or if you’re launching something new and don’t yet have your competitors ID’d – let’s walk through a few questions to help identify them:Who else is doing what you’re doing, or striving to do? Who is the closest competitor? Also consider less obvious competitors, such as brands or products that may not replicate your product, but may feel similar in tone. And also think about those whose work is similar enough – or those who have a similar ethos. Consider that they could pose a future threat if they develop something new.
- Next, ID their strengths and weaknesses. This is where SWOT comes in. Literally write down each competitor’s name and product, and then run a mini SWOT on them.
- Pay attention to the details. What kind of marketing campaigns are your competitors running – and in what channels? What keywords are they using? What obvious keywords are they avoiding? Take note of your observations. You may make some very interesting discoveries here, so take detailed notes.
- Get a little help from your friends – and the Internet. You don’t have to do all of this research on your own. Turn to your colleagues, for example, the folks on your R&D team who came up with the product you’re in charge of marketing. It’s possible that they’ve done similar competitive research while concepting and creating the product. Get your hands on that material.Also, if you’re willing to put in the work, you can access a wealth of consumer-authored and brand-authored information online. As we know by now, today’s consumers do a ton of pre-work before they make a decision. In fact, I just did this exact thing: I’m in the market for a new blender and spent a good chunk of time doing research online before making my decision. I read the product descriptions, reviews, and user comments. I paid attention to all the details – the good, the bad, and the ugly.
As a marketer, you can use that same data and strategy to gain understanding of what customers want and how your competitors are responding. For example, scour competitors’ product reviews and their social media feeds. Pay attention both to what consumers are saying and how brands respond. These primary sources may be time-consuming to shuffle through, but yield invaluable results.
- Turn your research inward and make recommendations. The ultimate goal of a competitive analysis is to better position your own product against the competitors. Once you’ve found your competitors and what they’re doing, turn your focus to your own brand or product. How do you – or could you – do things differently? What are your strengths above theirs – and/or differentiators? For ideas, turn back to your SWOT here and look at your “S” and “O” categories specifically. What have you learned, and what could you try?Remember to use your knowledge for good. It’s worth repeating: Once you’ve done your work, you will likely have a great playbook of what the competition does and how they do it. Now’s the time to put your white hat back on and remind yourself and your colleagues of good business practices. Ideate ‒ don’t imitate.
- Formalize your findings. Take copious notes, and pull them into a format that you and your colleagues can use. It might look like this:
- Devote one page (or slide) per competitor/product.
- On each page note the competitor’s name and the specific product name. Include a quick SWOT grid. Note three top-level findings.
- In your conclusion, summarize your findings and recommendations.
Don’t forget to add the date of your research; a lot can change in a few months, so it’s helpful to notate when research was performed.
Practice makes perfect
The first time you perform a competitive analysis can be shaky. Personally, I ended up with a laundry list of brands, products, and notes. Once I went through the exercise a few more times, I got more of a rhythm with what I was trying to do – and what kind of information I was looking for. As with anything, practice makes perfect (or at least makes you more comfortable).
For bonus points, look outside your own rabbit hole. Try performing a competitive analysis for a totally unrelated company or industry. For example, if you’re a marketer for software, pretend instead that you’re marketing a specialty clothing line. Or do a competitive analysis for a retail coffee chain, or an e-learning platform, or … the possibilities are endless. I recommend doing these exercises to get practice with the craft of competitive analysis, but also to gain perspective and inspiration. You may even want to do a competitive analysis of an industry or company that you really want to work in. This way you have it in your back pocket when you secure an interview!