Who Won Black Friday and Cyber Monday? Sentieo Uncovers 5 Winners and 1 Loser

For investors in Consumer Discretionary and corporates operating in that space (retailers, consumer products), monitoring various sources of alternative data is of paramount importance. Search trends (and their year-over-year change), Twitter mentions (with year-over-year changes), web traffic (also with year-over-year changes) alone or together can provide important, real time insights during this critical holiday shopping period. We used a number of these sources, by themselves and combined, to look for standout products and companies. 

All indications point to the 2019 holiday shopping season remaining very strong, with 3.8%-4.2% spending growth expected. Online spending on Thanksgiving Day itself was up almost 15% to a new record high. Adobe’s shopping tracker reported $7.4 bn in online spending on Black Friday, up almost 20% YoY, and another $3.6 bn Cyber Monday, up 18% YoY. 

5 Holiday Winners

 

1. Peloton

Maybe you’ve seen The Ad, and maybe you’ve read about the controversy. Perhaps you liked the ad, or perhaps you did not. But what really matters here is that Peloton, a relative newcomer to the public markets, was able to dominate the conversation for a few days (!) and insert its $2,000 stationary bike + $39/mo sub plan as a possible holiday gift for many consumers this year. Step aside, Lexus! 

Interactive public chart viewer 

 

2. Roku:

We really liked Roku in early 2019 when we released our Sentieo 11 alternative data stock picks: it was the top performing pick in H1. We also included it in our July 13 picks for H2 2019. We discuss the idea in detail, as well as the overall alternative data methodology used, in this webinar from July with our CEO/co-founder Alap Shah and VP of Product Arib Rahman. 

Our alternative data composite index (a multivariate regression for the most predictive basket weights of the available data sets) is pointing to an exceptionally strong holiday season for Roku. We would not be surprised to see a revenue beat (street estimates of quarterly revenue are the dotted green line in the chart; actuals are the solid green line). 

Interactive public chart viewer 

 

3. Airpods: 

Airpods are… seasonal? 

The bluetooth audio/mic earbuds by Apple are hot this season much to our surprise. (Did everyone lose theirs at the same time, or are they now a discretionary accessory?) We can’t be sure, other than what we see in the data: highest ever search interest and seasonal high Twitter mentions (30-day moving average) for the product (starting at $159 in the US for the basic version).

Interactive public chart viewer 

 

4. Rosetta Stone:

Rosetta Stone is a language learning software company that has had its ups and downs over the last few years. We spotted the alternative data index acceleration in our Screener, and we were immediately interested. 

Interactive public chart viewer

The acceleration is due to web traffic: we are seeing a 100%+ spike on a year-over-year basis on rolling 30- and 91-day moving average basis. 

Interactive public chart viewer  

 

5. Five Below:

The discount retailer was also another one of our H1 picks, like Roku and Nintendo above. They reported strong results on December 4th (retailer fiscal years are generally on a January year-end so their earnings season is later than most: see our Q3 machine learning/NLP Transcript Smart Summaries here on real Q3 calls from Facebook, YUM! Brands, Beyond Meat and Merck). We see strong data sets for Five into the holiday season (yet to be reported). 

“Stacked” search trends show annual search trends for the entire year, “stacked” on top of prior years. We see that 2019 has been very good for Five Below, including the current holiday season. 

We are also seeing good web traffic. 

Interactive public chart viewer 

The momentum has meant a great looking Sentieo alternative data composite index for the FIVE revenue growth for the quarter that was just reported, and likely continued strong performance through the holiday season. 

Interactive public chart viewer 

1 Holiday Loser: Victoria’s Secret 

Our pick for the loser this holiday season is Victoria’s Secret, a unit of L Brands. The lingerie brand has seen years of declining search interest and weak comparable store sales, as shown in the table below, which uses our one-click table chaining and extraction tool.

We are seeing very weak search trends compared with the last 10 years!

Note that VS is one of two brands for the parent company, L brands. The other one, Bath and Body Works, has been strong over the last few years. 

To try analyzing alternative datasets for yourself, try Sentieo today.

Taking a Data-Driven Approach to Alpha Generation: Part 2

Over the last few years, we have seen the not-so-gradual evolution of investment marketing, from the rise of indexing and quantitative strategies to the explosion of alternative data and fee compression. The need to “research different” to maintain your edge is greater today than ever before. But despite the changes, there remains one major thing all successful investors do: correctly identify trends. 

Over the coming weeks, we will cover three different strategies to overcome overreliance on hunches, intuition, or biased views and start quantifying and visualizing investable trends, to ultimately generate alpha faster. You can skip ahead and read the full report here.

A couple weeks ago, we covered everyone’s favorite industry buzzword: alternative data. Today, we switch gears and focus on how to capitalize on your internal datasets for alpha generation.

When looking to “supercharge” research, it’s important to include internal proprietary data sets, such as search statistics on corporate documents, like earnings call transcripts. Unlike alternative data sets, which are widely available for purchase, document search statistics are your own internal proprietary data set. These can be a powerful source for trend identification and verification.

One example of this is the short-lived “gluten free” fad. A quick search of conference call transcripts that mention “gluten free” show that while gluten intolerance is in fact a real issue, it really only impacts a small percentage of the population. As a result, the corporate response clearly came and went

Another example is the opioid crisis. In this case, search statistics help quantify metrics for risk management purposes. For example, we can see that the number of 10-K filings with Risk Factors that mention “opioid” or “opioids” have increased substantially over the years

Search statistics for documents can also be combined with financial metrics to spot trends in an industry group as a whole. For example, combining restaurant industry transcript mentions of “wage inflation” against the average adjusted EBIT margin for a group of US casual dining chains shows that the margin peaked and started to decline shortly after document statistics pointed to increasing wage pressures

But these internal datasets can do more than just verify or quantify trends, they can also help you spot trending terms as well as “equivalents” to help you generate ideas and develop a thesis. For example, looking at all corporate documents for YUM! Brands (global parent of KFC, Pizza Hut, and Taco Bell), we can identify company-specific trending topics.  

This same concept can be applied to searches for document types: searching 10-K filings with the SEC for Vietnam and other Southeast Asian countries are trending in the mix.

Most importantly, search statistics for documents are entirely your own proprietary data set. Because they’re not available from the data vendors, they are your own competitive advantage and enable you to preserve your edge for longer.

Next week, we’ll cover how to create powerful visualizations over time using sector-level financial metrics. If you can’t wait that long, you can download the full report here.

Taking a Data-Driven Approach to Alpha Generation: Part 1

Over the last few years, we have watched the not-so-gradual evolution of investment marketing, from the rise of indexing and quantitative strategies to an explosion of alternative data and fee compression. The need to “research different” to maintain your edge is greater today than ever before. But despite the changes, there remains one major activity that all successful investors do: correctly identify trends. 

Over the coming weeks, we will cover three different strategies to help you surpass your overreliance on hunches, intuition, or biased views — and start quantifying and visualizing investable trends (so you can ultimately generate alpha faster). You can skip ahead and read the full guided report here.

Today we’re covering everyone’s favorite industry buzzword: alternative data.

“Alternative data,” a broad term that refers to newer data sources, has become increasingly popular over the last few years. From credit card data to search trends, app rankings, and even Twitter mentions, alternative data has become a buzzword as well as a useful tool for research. 

Alternative data can be powerful. But to identify alpha-generating trends, we need more than just one data source, and we need to combine data sources. For example, let’s take a look at clear aligners (the clear plastic trays used for teeth alignment). You might be familiar with the leader in the space, Align Technologies (makers of Invisalign), but there are a good number of recent entrants in the space. These include SmileDirectClub, a DTC (direct to consumer) clear aligner company that recently completed an initial public offering. 

Starting with a very basic alternative dataset, search trends, we can gauge both the direction and the seasonality of the topic. An important point to make here is the distinction between search term (the exact term) versus the broader search topic (which bundles a number of related search terms together). 

Plotting the search interest, we can see that interest in clear aligners has been growing very steadily for the last few years, with peak levels (indexed to 100) happening in 2019. We can also see distinctive seasonality: a drop off into the December holidays, followed by a spike to new highs in January, consistent with self-improvement trends post-New Year.  

Interactive chart http://snt.io/KEF8ejHb8

But search is just a part of the picture. 

Using Alexa web traffic data, we can combine the search interest with web traffic data for the major players in the clear aligner space. While there are “web traffic share” gains and losses, we can see that, as a whole, traffic is increasing across all players: Align, Smile Direct, Snap Correct, Candid Co and Smile Love. 

Interactive chart: http://snt.io/MfF8emuTG

As the final step here, we can overlay the financial and valuation metrics for Align Technologies as the public company in the group with the longest trading history. For clarity, we are “hiding” the non-ALGN traffic metrics. We can see that the search trends correspond very nicely to quarterly revenues (2-year correlation is 0.8), while valuation (EV/Sales here) seems to move based on web traffic direction on shorter time frames. 

Interactive chart http://snt.io/BSF8epB5B

You don’t have to focus on individual securities to use trends in alternative data—it can also be used to detect changes in macroeconomic trends. 

For example, using Twitter as a “repository” for live conversations, we can see that tweets containing “new job” (30-day moving average for smoothing) foreshadowed the increase in the non-farm quits rate by months around 2012-2013. 

Interactive chart http://snt.io/NcF8et44S

Effortlessly combining traditional and alternative data sets in compelling visualization lets you not only see more but speed up your idea velocity in the pursuit of alpha. In the case of clear aligners we can see that there is a very strong, and growing, underlying demand for more convenient, “tech first” cosmetic dental care.

Next week we’ll cover how to capitalize on your internal datasets for alpha generation. If you can’t wait that long, you can download the full report here.

[Videos] How to Use Sentieo’s Table Explorer To Extract Data and Save Hours

In this “how to” post, we will review how to use our Table Explorer table chaining function in SEC documents, as well as the major updates we integrated with our recent v3.9 release. 

Broadly speaking, you can do a LOT with tables in Sentieo. 

  1. Export one or all document tables into Excel 
  2. Search only within table (s) using our popular “in:table” shortcut 
  3. See side-by-side historical tables (very useful for annual filings like proxy statements)
  4. Export PPT/PDF tables to Excel 

But none of these functionalities come close to what Table Explorer can do. Table Explorer uses a machine learning model to identify, chain, and visualize reported data. This is done in an auditable, transparent fashion, with added one-click flexibility for elements such as YoY and QoQ changes, common size statement conversion, and more. Analysts save countless hours with this ability to quickly chain tables, as well as visualize data and trends.

We will start with a simple example: the income statement. Table Explorer chains either 8-Ks, or 10-Qs/10-Ks. Since Q4s are not always reported, we create “synthetic” Q4 statements with one click. We use Adobe’s income statement as an example. Watch the video to find out more. 

In this second video, we cover the balance sheet. Similar to the income statement video above, we have an easy adjustment for Q4 balances. We go over the Netflix balance sheet, starting with their most recent filing. 

In this next video, we cover the cash flow statement. Cash flow statements are often presented in a cumulative fashion: 3-month in Q1, 6-month in Q2, 9-month in Q3, and 12-month in Q4. In this specific example, we use Mondelez International, the global snacking giant. We convert to quarterly with just one click. (We even came up with a new word for this function: “to quarterize”).

Table X does not stop here. You can export your chained data to Excel and to our data visualization tool, Plotter, for further work. You can save the data into your Sentieo Notebook to add to a full thesis.

The video below reviews these steps. We extract and chain Adobe’s income statement, convert it to “common size,” then visualize and export the percentage split of service vs. product revenues for the company. We then export to our main data visualization tool Plotter, where we overlay two of the thousands of data sets included in Sentieo, and finally, we save the chart in our Notebook for use in our full-blown thesis on the company.  

Table X works on other tables too. For example, you can extract your own KPIs or any other table-based data. While some KPIs might be available from the standardized data providers, not all are, and extracting your own means greater confidence in your data through our on-screen auditing function. In this fifth video, we show you how. We extract and chain Chipotle’s KPI tables, and we quickly visualize the trends in average unit volumes and comparable store sales. 

After Table X was released to rave reviews earlier this year, the number one request was handling line item renaming. With the latest product release, we created a Row Merge/Demerge capability to address this workflow. We show you how in this video. In the example here, Crocs, a footwear company with a wholesale and a retail business, reports a quarterly store count by geographic region. However, the company changed its line item name from “Europe” to “EMEA” recently, creating a discontinuity in this specific data set. We show how to merge the two rows and how to check that the mapping is correct. 

In the final, and most technical, video we show you how Table X handles a specific type of table called a “roll-forward” table. A standard use case for this is a company reporting a change in balance over the quarter, where we have the starting balance on top of the table, any changes below that, and then we have the ending balance at the bottom. In this specific example, we chain Chipotle’s unit openings/closings table from their 10-Qs and 10-Ks, and then make a few quick adjustments to add a Q4 column with the correct starting/ending balance, and intra-quarter additions. We also demonstrate the importance of visualizations. Unit openings are clustered in Q4, and this plays a role in forecasting the annual revenues due to the backend loading. 

To find out more about Sentieo’s Table Explorer, or any of the other features in our complete workflow research platform, please get in touch with us. 

 

Facebook (FB) Transcript Smart Summary™ — Legal Discussions Get Longer and Instagram Innovates

In this final installment of our earnings season blog posts (informed by Sentieo’s transcript Smart Summary™), we’re covering Facebook (FB).

We kicked the week off by “getting to the meat” of Beyond Meat’s call on Monday night. While growth and margin expansion were strong, our keyword surfacing highlighted McDonald’s as a key topic.

We then looked at Tuesday’s transcript from Merck. While its VIP drug Keytruda is growing nicely, the Smart Summary™ highlighted pricing pressure concerns.

YUM! Brands, the parent of KFC, Pizza Hut and Taco Bell, reported on Wednesday morning. Smart Summary™ was useful to check on the many moving parts of this global, multi-brand QSR chain — from regional comp sales across brands to unit growth. Our sentiment scoring noted a particular analyst question about system-wide margin pressures. (Because it was deep in the transcript, a human reader could have missed this). Our blog posts above also feature short videos, with our Head of Research Nick Mazing reviewing how transcript push notifications work in Sentieo, and what he chooses to hone in on for each ticker.

For Facebook, since it is a well-covered company, we will focus on a smaller set of topics: 2020 guidance, regulatory discussions, and product innovation. 

On the guidance side, we see 2020 revenue growth deceleration, but this is widely expected, as Facebook is already large. We also see concrete expense numbers; there have been questions regarding Facebook’s investment in combating fake news and other undesirable content. 

There is a very lengthy regulatory discussion on this call. We see various entities/regulations mentioned on this call. Investors pay close attention to the regulatory environment. Heavy regulation tends to benefit the incumbent players, and Facebook is poised to lock out threats, similar to what happened in the US with the cigarette companies and the “Master Settlement Agreement.”

Contrast this Q3 2019 regulatory discussion versus Q3 2018. We can see below that the category in the Smart Summary™ was blank. 

We can also see the spike in Facebook’s 10-K/10-Q mentions of FTC. They went from single mentions to 27-30 more recently. 

We can also see the financial impacts. The FTC levied a fine in the billions of dollars, which we can see when we chain together the breakout table in the latest 10-Q with the same table in prior filings. 

The last highlight from this call is product, specifically Facebook’s innovation around Instagram. Our NLP-based keyword surfacing picked up the conversation there as the top keyword on that call. 

 

Check out our quick video walkthrough on Facebook.

Smart Summary™ is just a part of Sentieo’s research platform. To find out more about us, please get in touch

YUM! Brands’ Q3 Transcript Smart Summary™ — Extracting the Essentials From a Call With Many Moving Parts

This week, we continue to use Sentieo’s transcript Smart Summary™ to highlight selected reporting companies. We apply machine learning and natural language processing to create a more efficient and a more repeatable process. On Tuesday, we highlighted Beyond Meat (Nasdaq:BYND), and on Wednesday, we looked at Merck. (While Keytruda continues to power through, we picked up pricing pressure warnings for 2020.)

In today’s highlight, we are looking YUM! Brands (NYSE:YUM), the parent of global restaurant chains KFC, Taco Bell, and Pizza Hut. The company reports many metrics about its business: domestic (US) and international unit growth, comparable store (“like for like”) sales growth, and “systemwide” sales growth (reflective of the health of the franchisee system). 

Additionally, the company has a minority investment in order aggregator/delivery company Grubhub, which further complicates the financial reporting, as YUM reports mark-to-market adjustments on top of the operating earnings. 

In this post, we will share our highlights from the Smart Summary™ PDF that we received in our email inbox a few minutes after the original transcript came through. (The Sentieo platform is much larger and more interactive that what you see in this post, so we encourage you to check it out.)

With Smart Summary™, the transcripts are parsed by a ML tool which classifies and scores sentences based on broad classifications, such as Guidance or Legal. (See below). Smart Summary™ runs a layer of NLP processing on top of that, classifying sentiment (positive/negative/neutral), looking for “deflection” statements, and surfacing keywords. 

Looking at the YUM Q3 call Guidance section, we immediately spot the big delta between the GAAP and non-GAAP earnings. We also see the red (negative) outlook for Pizza Hut US, a unit that has been weak for a while. 

As we mentioned earlier, the transcript (as well as the press release) are very KPI-heavy. The convenient sentence extraction and classification helps you get the full picture faster.

We also note a number of negative sentiment questions from the analysts on the call, with the most “negative” analyst question being about system-wide margin pressures. 

The keyword surfacing logically picked up Same Store Sales, Net New Units, and System Sales.  We are highlighting just one of these here; unit growth keeps up, so the sentiment overlay is green. 

Watch our short video walk-through:

To find out more about the Smart Summary™ and all of Sentieo’s other AI-powered features, please get in touch

 

Merck Q3 Transcript Smart Summary™ – Keytruda Growth Continues, But Pricing Pressure Looms

As a part of our earnings season coverage, we’re continuing to use Sentieo’s transcript Smart Summary™ to highlight selected reporting companies. Smart Summary™ applies machine learning and natural language processing to create a more efficient and a more repeatable process for transcript analysis. Yesterday, we highlighted Beyond Meat (Nasdaq:BYND), where we definitely went beyond the meat of the matter and into the details.

In this piece, we will take a quick look at pharmaceutical giant Merck. We are using the newly-released email push notification functionality for the Smart Summary™ transcript, which provides users with both the summary and the full transcript in a single PDF. We’ll go through parts of the Smart Summary™ PDF for Merck, but you can view the full report here.

The key headline from the MRK release was that the company increased its 2019 non-GAAP EPS guidance. We can see this clearly in the Guidance section of the summary. 

In the KPI section, we see several interesting tidbits: tax rate, Animal Health division data, plus comments on Keytruda, an important drug. 


The pricing pressure comment that was ranked high in Guidance was also notably ranked high in the Products/Markets section as well.

On the Legal/Regulatory front, we see comments around drug approvals. 

In addition to the ML-based classification, we also apply NLP (Natural Language Processing) in three dimensions: sentiment (positive/negative), deflection (language associated with “deflection”), as well as keyword surfacing. 

In Merck’s transcript, we see a lot of highly-scored positive sentences (100 out of 100). 

On the negative side, we see the pricing pressures in 2020 comments (already picked up and classified by the ML layer) marked as mostly negative.

We can also see that pricing is the most negative analyst question as well!

The top keyword based on our algorithmic keyword surfacing is Keytruda, a relatively new but popular drug.

The second and third keywords are also product related. Below we see an active discussion around Gardasil capacity.

And the focus on Keytruda and Gardasil is justified. Using Sentieo’s Table Explorer table chaining tool on the 8-K filing, we can see that both drugs continue to grow, with Keytruda now approaching 30% of revenues. 

Watch the video below to see it in action:

View the full report on Merck here, and try using Smart Summary™ for yourself by signing up for access for Sentieo.

“Getting To The Meat” of the Beyond Meat (Nasdaq: BYND) Earnings Call

Beyond Meat (Nasdaq: BYND) reports triple-digit revenue growth,1600 bps margin improvement, ups FY guidance — all of which were picked up and highlighted by Sentieo’s ML/NLP-based transcript Smart Summary.™  

Sentieo’s transcript Smart Summary™ uses machine learning and natural language processing to help you go through transcripts faster, highlight important information you might have missed, objectively quantify sentiment and deflection statements, and surface keywords and keyword trends. All of this happens at the click of a button on our platform, and for clients with enabled alerts, also via email.  

You no longer have to read transcripts chronologically; you can sort the document based on classifications, as well as “x-ray” for trending (and disappearing!) keywords. 

Today we will highlight BYND’s results from Monday, October 28, 2019, as we saw them in our email alert with the PDF document. 

Using machine learning, we trained a tool to read and classify sentences in transcripts in several buckets, including Guidance, KPIs, and more. 

Right on top, we can see the robust guidance increase by the fast-growing meat alternative company. 

We can see the current quarter results’ highlights in the KPIs section:

The massive 1,600 bps gross margin expansion was picked up there, too. 

Our NLP processing picks up Deflection as well as Positive/Negative sentiment. We can see some softly optimistic language under Deflection. 

The Negative sentiment highlights include competitive pressure from incumbents as well as lower volume in newer points of distribution. 

One keyword that stood out is McDonald’s: a high profile restaurant partner that is running a very limited test only in Canada. We see the analyst being very inquisitive about it. 

To view the full Smart Summary PDF, search for the transcript in Sentieo and click on the Smart Summary icon. If you don’t have access to Sentieo yet, view the full summary here.

You can also check out the video below for a Sentieo walkthrough!

Stay tuned for our next earnings Smart Summary™ tomorrow!

Want to try Smart Summary™ for yourself? Get started with a free trial.

Is Pumpkin Spice Over?

About a year ago, we wrote a popular blog post on pumpkin spice season. Based on Twitter data and search trends, we could see that pumpkin spice season had started earlier than ever, and was bigger than ever. 

Today, we declare that pumpkin spice is over, using the same data sets. 

Looking at the stacked search trends below, we can see that pumpkin spice was off to an ever-earlier and stronger season in August, running well above prior years (see light blue line). However, the trend peaked below last year’s peak (momentum investors know this sign), and has been tracking below recent years since then. 

We tracked down a couple of notable pumpkin spice season “kick-off” events this year.

Convenience store chain 7-11 announced that their pumpkin spice lattes were back on August 14, 2019.

Dunkin’ Brands (parent of ice cream chain Baskin Robbins) did not highlight the flavor until August 26, 2019.

Things really picked up in early September with releases from Hostess Brands (Nasdaq: TWNK), Restaurant Brands’ Tim Horton’s division (NYSE: QSR), Krispy Kreme, and others.  

Perhaps the biggest success story this pumpkin spice season came from Hormel (NYSE: HRL), which released a limited edition version of their legendary Spam: “[the] limited edition flavor features a blend of seasonal spices including cinnamon, clove, allspice and nutmeg to give it a subtle sweetness.” The September 23rd release was followed by another press release a few hours later mentioning that the $8.98/2-pack item was sold out from both Walmart’s e-commerce site and spam.com in under seven hours. 

Photo source: Hormel PR

For the final word on pumpkin spice, we used our Twitter data integration to see the trends around Starbucks (Nasdaq: SBUX), and their high-profile pumpkin spice beverages. Based on Twitter mentions, we note that, very much like the search trends, YoY mentions are down, and with a shorter “tail” versus prior years. Pumpkin spice just isn’t that big of a deal any more. (Interactive chart link)

To find out how Sentieo’s full workflow solution can help you harness multiple data sets, track promotional intensity, create visualizations, and more, please get in touch

 

Which Retailers Are Winning This “Back to School” Season? Combining Document Search and Alternative Data to Find Out

Our first step to analyzing how retailers are doing during this “Back to School” season is to use our industry-leading Document Search to find out which retailers are talking about “Back to School” in the first place. If keyword mentions appear in filings or in transcripts, then we know that we should be taking a closer look at that company. 

We start in our Document Search without tickers (since we want keep the search broad to firstly see who mentions “back to school”). We combine our in:CF (in-filings) and in:TR (in-transcript) search for exact match “back to school.” We can also see our machine learning-suggested synonyms as well, though here we are not going to use them. 

We add two filters: one is an industry filter (Consumer Discretionary and Consumer Staples), and the other is a geographic filter (US). 

In our next step, we create our “Back to School” retailers watchlist by simply saving all companies that have positive hits in our search. We started with a fairly broad theme that is now confined to a watchlist with specific companies on it. 

In our next step, we name our Back to School watchlist, configuring any alerts that we would like to receive, and saving the watchlist with alerts preferences. 

In our next step, we brought in our just-created Back to School list in our customizable Dashboard, where we are looking at a few things related to our composite alternative data index. (Watch this recorded webinar to find out how our alternative data index takes several sets, calendarizes them properly to the reporting periods, and then compares the “index” to the consensus estimates).

First, we see which is the optimal metric against which the index works best, based on past performance. Since these are retailers, we can see that comparable store sales, a standard KPI for the industry, works better than revenue for some. Second, we see the R-squared that our index has against that optimal metrics: a higher number here indicates a higher predictability. 

We dig deeper by checking the individual dataset metrics on a monthly basis for YoY changes (in this case, we are showing monthly YoY percentage change in search trends and page views) along with the earnings dates.

We see that there 18 retailers reporting in the next few days (the week of August 26, 2019, and the week of September 02, 2019). They are: CAL, BNED, TIF, EXPR, FIVE, TLYS, SCVL, AEO, GCO, BBY, BURL, DBI, ANF, DLTR, PVH, ZUMZ, FRAN, and VRA. Since these retailers’ fiscal year typically ends at the end of January (vs. the standard December for the majority of publicly traded companies), the Q2 numbers are for the quarter ending in July. So the “back to school” period is somewhat split. But investors do expect QTD color for Q3, as well as guidance updates. Since the Sentieo team has decades of buyside experience (all product managers are former buyside and/or sellside analysts), we know that we can eliminate TIF, BNED, and FRAN from the list. TIF, a high-end jewelry retailer, is not really driven by BTS, while BNED and FRAN are “special situations” currently. 

With our slimmed-down, “actionable” list of 15 stocks, we took a look at what our composite index looks like YoY (used for the more predictive metric, revenue or comparable store sales growth).  

We see the potential for strong overall YoY revenue growth in FIVE, PVH, DBI, AEO, CAL, ZUMZ, and VRA. We see the best potential for comparable store sales growth for BBY, BURL, SCVL, and DLTR. 

Taking this a step further, we can look at past performance by adding the R-squareds to our list (higher = more predictive). Our confidence is highest in the YoY revenue growth performance from FIVE and AEO, and for comps, in BURL. 

To find out more on how you can compare the alternative data composites against the analyst consensus numbers, please see our white paper and webinar from a few weeks ago, or request a trial with a product specialist

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