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

New call-to-action

Just How Unusual Are the Related Party Transactions Disclosed in The We Company’s S-1 Filing?

To find out, the Sentieo team took a look at other recent high profile IPOs to compare against what we saw in The We Company’s expansive 800+ page S-1 filing. The document also contains 198 separate tables. Inside the filing, the Company (proposed ticker: WE) spends ten pages on discussing various transactions with “related parties.” Contrary to widespread understanding, related parties are not limited to just insiders and large owners. In this case, the underwriters of the equity IPO are also considered related parties, since there are multiple non-IPO business transactions that have occurred prior to the filing. If you are interested in finding out more about the regulations, there are extensive SEC publications regarding these disclosures (for example, there is a 436-page PDF published by the Commission on the topic).

Part of what we see is attributed to the fact that as a real estate company, WE uses bank financing. Another aspect is that as a remarkably fast growing company in the physical world, the company has needed trusted JV partners in different geographies.

WE has disclosed various transactions with four “levels” of insiders: the founder, Adam Neumann and his family, its executives, its pre-IPO investors, and its banks. In fact, “related party” or “related parties” is mentioned 110 times in the document.

In the first group, WE has business relationships with the founder across several dimensions: it leases a small number of buildings from him, there are unusual supervoting stock, succession, charitable giving, real estate, and compensation arrangements. There are several family members employed or doing business with the firm. The founder was paid almost $6 million for the renaming of the company (since he personally had a company called We Holdings), and he has borrowed several times from the company, and separately from its offering underwriters.

WE also disclosed related party transactions with several executives, including loans and bonuses that were used to repay these loans.

SoftBank and Hony are investors in WE but are also partners for WE in its various Asian joint ventures.

The IPO underwriters (a collection of bulge bracket banks) also have several “related party” disclosures: ownership of preferred stock, loans to the company, as well as personal loans to the founder: almost $500 million secured either by WE stock or by personal properties.

So how unusual is this level of related party transactions in recent high profile IPOs? We took at look at Slack, Uber, Lyft, Chewy, Pinterest, Levi Strauss, and Zoom Video to get an idea. 

In Slack’s filing, we see a few mentions. There have been several rounds of convertible preferred financings and executives selling shares. There have been transactions with Square (since the Square CFO is on the Board of Slack, she’s a related party), some content partnerships with the wife of the CTO and the former domestic partner of the CEO, and the son of a BOD member works at the company. The VC investors are also partners with Slack in an “in house” VC fund. (We dug deep into Slack’s business model back in May).

Uber, like Slack, has had several rounds of convertible preferred financing. Its executives, like Slack’s, have had pre-IPO liquidity events with company involvement. It has a co-investment with Softbank (and Toyota) in an AV venture. Uber has a relationship with Google Maps, Google’s ad business and Google Pay, all owned by Alphabet, an investor. The daughter of an executive is employed at the company. There are a few other bits and pieces, like their relationship with DiDi.

Lyft’s related party transactions are almost a carbon copy of these at Uber: investors with convertible preferreds, and business relationships with several related parties, such as Google, General Motors and Rakuten. (Our five big AV takeaways from Uber’s and Lyft’s filings are written up here).

Chewy, the fast-growing online pet product retailer, was mostly owned by pet product physical retailer PetSmart. Its related parties disclosure is relatively plain, and almost entirely focused on its operational relationship with PetSmart: purchasing, product, tax and governance matters, not unusual in the case of subsidiary IPOs. (Our read of Chewy’s full IPO filing is here).

Pinterest, similar to the tech companies described above, has disclosures around its relationships with its VC investors, and there is one family member of an executive employed in a non-executive function. (We wrote a very long post analyzing Pinterest after the IPO).

Beyond Meat disclosed a consulting agreement with its Chairman and an advisory contract with a Board of Directors member. There was a one-time consulting agreement with another BOD member (the former CEO of McDonald’s), and loans to BOD members that were repaid in 2018. (We recently dug around Beyond Meat’s secondary offering documents).

Levi Strauss & Co. has a fairly straight-forward 2-page disclosure: the descendents of the founder have certain rights as shareholders, some executives have sold stock back to the company, and there is some overlap between the executive team of the company and that of the Levi Strauss Foundation, to which the company also donates. There is one former BOD relative employed at the company.

Similar to LEVI, Zoom has a short disclosure doc: relationships with the VC investors, its founder had sold some stock to a fund and had a loan in 2015-2016, and a BOD member is from Veeva, which is also a small client.

It is fair to say that WE’s relationships with its related parties go well above and beyond what we have seen in the other recent high profile initial public offerings. The most common are: governance arrangements with pre-IPO VC investors, followed by ordinary course of business relationships with investors such as Google (it is hard for a consumer-facing business to avoid working with Alphabet properties), and finally, cases of founders and executives getting some liquidity for their equity stakes over the years.

New call-to-action

Obscure Berkshire Hathaway Filing Reveals What Warren Buffett Thinks About Kraft Heinz

Berkshire’s investment in Kraft Heinz has not gone smoothly. After taking Heinz private together with private equity investor 3G in 2013, Berkshire invested additional funds in the takeover of Kraft Foods in 2015, and was ready to finance a quickly-withdrawn $143 billion offer for Unilever.

Things have gone south for KHC recently. The company’s stock price and valuation have shrunk considerably over the last two years versus that of its peers. We can see KHC’s EV/NTM EBITDA multiple (in thick red below) starting at the top of its peer group two years ago, but recently declining to very the bottom compared with other US food companies (interactive chart link). We do expect the multiple to move up somewhat over the next few days as analysts adjust their estimates after the call.

 

KHC also had to delay its financial reporting this year due to certain actions by former employeesthat led to small restatements, and more notably, the company took a $15 billion write-down of brand values and cut its dividend in February of this year. In the most recent call on August 8, 2019, the company, now with a new CEO from AB In-Bev, did not provide guidance, took additional impairments, and warned of future impairments.

The travails at KHC are a topic of increasing concern to Berkshire Hathaway investors. The number of mentions of Kraft on the most recent shareholder meeting more than tripled versus last year to 27, up from just 8 in 2018.

Buffett himself did admit that Berkshire overpaid for Kraft but still thinks KHC is a “wonderful business.”

 

While getting color from the transcripts is nice, we were also interested in what is actually in the Berkshire filings, so we redlined the second quarter 10-Q filed with the SEC on August 5, 2019 against the Q1 version of the document. We saw a lot of new language around KHC.

What we’re seeing is a lower fair value of the investment (easily observable, since KHC is publicly traded). The carrying value was reduced because of the KHC financials’ restatement. We also see some puts and takes around the KHC YTD filings.

Most interesting to us was the inserted paragraph at the bottom; Berkshire reviewed KHC for impairments, and as of June 30, 2019, decided against it.

 

But Why?

The answer comes in a more obscure SEC filing, called CORRESP for Correspondence. CORRESP and UPLOAD are two forms of formal communication between the Commission and the filers. When a letter is directed from the SEC, it appears in filings as UPLOAD, and when the filer responds to the regulator, it is a CORRESP. 

Berkshire filed two UPLOAD and two CORRESP forms on July 24, 2019, though the exchange between the company and the regulator had taken place in May and June. 

The SEC was interested in how Berkshire was accounting for Kraft Heinz in the Q1 2019 10-Q filing. 

 

And here is how Berkshire responded in great detail to the SEC: KHC’s stock price decline and the length of this decline was not substantial enough. Also, the operating results, while currently poor, will be better (divestitures, brand power, reduced but not eliminated dividend)

 

On the following page, we see more information related to how Berkshire thinks about KHC: there are no plans to sell the stock at any time, and the KHC restatements are immaterial. 

 

Given Berkshire’s long involvement with Heinz, and then with Kraft Heinz, including Board of Directors representation, and its continuous investments in the space, we view these disclosures as material: Warren Buffett still thinks KHC is a long-term holding position that will recover.

New call-to-action

What We Learned From Redlining Beyond Meat’s (Nasdaq: BYND) Secondary Offering Documents

Meat alternative marketer and recent IPO Beyond Meat (Nasdaq: BYND) reported quarterly earnings a few days ago, and, concurrently, the company surprised the market with a secondary offering well ahead of the indicated 180-day IPO period (a rare occurrence in the last 10 years). The stock’s performance since its IPO has been stunning: the IPO priced at $25 on May 01, 2019, and the stock went up over 8 times leading up to the earnings announcement.

The secondary offering priced at $160, well below the closing price of $222.13, the last price before the Q2 results and secondary announcement on July 29, 2019.

The offering was mostly pre-IPO investors and insiders selling (3,000,000 shares), along with the company selling 250,000 shares itself to fund its operations:

The secondary came just as BYND’s market capitalization surpassed that of a number of consumer staples companies in the S&P 500:

(interactive chart link)

We were curious to read the secondary offering document using redlining, to see what has changed since the IPO documents. Keep in mind that there will be changes solely due to the fact that BYND is now publicly traded, versus the pre-IPO language. We redlined the secondary S-1/A filed on July 31, 2019, against the final IPO S-1/A from April 29, 2019.

 

Here are our notes:

1) Stunning distribution and sales growth, along with successful partnerships and major increases in media impressions:

 

2) Expanding distribution in Europe and growing product lines:

 

 

3) Since the company is now publicly traded, there is a new warning around stock price volatility and potential losses:

 

4) Certain US states have introduced legislature regarding what products can be called “meat.” We saw this reflected in the added “state regulators” to this risk factor:

 

5) The rapid distribution growth noted above has resulted in some shifts in the major distribution partners:

 

6) There are no written contracts with the US co-manufacturers (note the EU deal mentioned above):

 

7) Big drop in local unemployment in the area around their Columbia, MO, facility warranted an update in this risk factor:

 

8) Entirely new risk factors: the growth will not last forever, and there might be serious fluctuations in the results:

 

9) Negative development for BYND in a lawsuit brought against them by a former co-manufacturer:

 

 

10) A relatively new development in IPO filings is the disclosure of use of Professional Employer Organizations for a number of HR/payroll tasks:

 

11) Surprisingly, the number of pending patent applications have dropped:

 

12) Added language around compliance and internal controls:

 

 

13) As we saw in the price action after the secondary offering was announced, the share price can fall. There is added language around secondary offerings’ effects:

 

14) Since the stock is now publicly traded, there is a whole new paragraph on the effect of research analyst coverage:

 

15) We can see the company balance sheet pro-forma of the offering. Note the increase in Cash and Cash equivalents, along with the increase in Additional paid-in capital:

 

16) New language on revenue seasonality (“summer grilling season”):

 

17) The IPO also lead to a simplification of the capital structure of the business (also note the Warrant crossed off in the table above):

 

18) The company has a small balance on its revolving line of credit:

 

19) New obligations: a 5-year office lease and minimum purchase commitments:

 

20) There is a lot of detail on Sales and Marketing activity (of course, all numbers are up: sales team, promotional events, samples, followers):

 

21) Notably, no changes in the serious celebrity endorser line-up:

 

22) New executive hire and one anticipated Board of Directors departure:

 

23) There is quite a bit of detail around the lock-up agreement (which was waived for this offering)

 

24) The underwriters have the standard “greenshoe” option to sell additional shares: 

Get in touch with Sentieo to try your own redlining!

 

New call-to-action

Doing Better Analysis with Non-Financial Data Sets in Sentieo Plotter

Sentieo’s main data visualization tool, Plotter, contains a veritable treasure trove of data series. While some financial and valuation data sets are commonly available, Sentieo Plotter stands out with its numerous non-financial data sets that are available within Sentieo, or created by you. (For example, you may have pulled Document Search statistics or data generated from Table Explorer, our ML-driven SEC filings table chaining tool).

Sentieo users can also upload their own data sets. Your individual data series and full charts can be saved for future use. Your charts can then also be added to longer notes or a formatted thesis. They can also be shared with your team or publicly to non-Sentieo viewers.

Below, we’ll go over a few of the non-financial data sets that can help you do better analysis.

 

FRED Macro

We recently integrated around 1,000 of the most popular data series from the data site maintained by the Federal Reserve Bank of St. Louis. 

An analyst needs to understand the macro currents that affect the companies and sectors under her coverage. For example, below we plotted the Civilian Unemployment Rate from FRED vs. a simple average rolling NTM P/E of four staffing companies. We can immediately see that the sector valuation is very closely tied to the unemployment rate. 

Interactive chart link

 

Online Activity Data and the Sentieo Index

Sentieo Plotter pulls Twitter mentions, Alexa website traffic, Google Search trends, and Instagram mentions. Through our Mosaic module, we also run a multivariate regression on these data sets against Wall Street estimates for revenue growth and KPIs. This Sentieo Index is also available inside Plotter, ready to be displayed alongside other data sets. 

For example, here is what one-month moving average page views, search trends, and Twitter mentions of the recent IPO Fiverr look like. Plotter lets you customize your data set display options, including moving averages (for smoothing) or YoY change metrics. 

Interactive chart link

 

Document Search Statistics

Part of your own IP is your ability to see trends: but how can you quantify trends with non-financial data sets? One way is to generate your own document search statistics to see if documents with key term mentions are going up or down. For example, below we searched all restaurant transcripts for mentions of “delivery.” We can see a clear increase in document mentions starting in 2017.  

Interactive chart link

 

Company-Specific KPIs

In some cases, investors and industry analysts give more weight on KPIs and KPI trends. Sentieo Plotter has company-specific KPIs that can help users “paint a picture” of the underlying business trends. 

In this example, we have pulled KPIs (comparable store sales) for five casual dining stocks, and have done an unweighted average for our “custom index” to see what is going on in this segment of the industry. 

Interactive chart link

 

Custom KPIs

Not all KPIs that an analyst might find relevant are available through legacy standardized data providers, but are disclosed in company filings. Sentieo’s Plotter is integrated with Sentieo’s machine learning-based Table Explorer tool, which identifies and chains past tables in SEC filings. 

For example, here we are pulling Royal Caribbean Cruises’ APCD (Available Passenger Cruise Days) in a few clicks into Plotter.

Interactive chart link

 

Linguistic Sentiment

Sentieo uses NLP (Natural Language Processing) for analyzing quarterly transcripts (management and analyst sentiment + spread between the two, along with keyword surfacing for both groups). These sentiment metrics can also be pulled into Plotter. For example, here we are looking at Tesla’s management sentiment reaching a high point when reported sales growth reached its highest point (summer of 2018, when the company started filling its backlog). 

Interactive chart link 

 

Your own data sets

Sentieo Plotter enhances your workflow, efficiency and visualization capabilities with two other very useful functions: uploading your own data in CSV format and giving you the ability to save generated data sets for quick recall in other Plotters so that you can re-use your IP. 

What we have seen is that some Sentieo clients upload their own proprietary data to combine with and visualize against what Plotter already has. Other clients like using publicly available but obscure data sets, such as very niche US Census data or BLS statistics. 

Sentieo can do a lot more than just data visualization. Please get in touch to request a demo

 

New call-to-action

Disrupting Transcripts: Spike In “Deflections” on Tesla’s Latest Call

In our final earnings week blog post discussing our new, machine learning-based transcript Smart Summary™, we’re taking a look at the electric car maker, Tesla. The company reported earnings and held its regular quarterly call after market hours on Wednesday, July 24, 2019. The stock dropped immediately upon the results release, and continued dropping during the call and into the next day.

While every quarter at Tesla has many puts and takes (like international shipments, tax credits, specific model volumes), what really stood out for us when we applied the Smart Summary™ was the really serious increase of “deflection” instances quarter over quarter. Our tweet on the topic got almost 10,000 views in a few hours:

 

So what is “deflection?”

Our new transcript Smart Summary™ tool has two underlying mechanisms. One is based on a specially trained machine learning system in which we, mostly former buysider product managers, trained the system to parse and classify sentences into a large training data set across industries and companies. This tool automated what we used to do during earnings season, and will only get better as our users submit direct feedback. Transcript Classifications are not mutually exclusive; a sentence might often be a Guidance and a KPI sentence at the same time. 

The second mechanism powering Smart Summary™ is natural language processing. We have had overall sentiment reports for management and analyst teams, and keyword surfacing applied to transcripts in the product for a long time. What we’re introducing with the Smart Summary™ is the scoring of specific sentences (positive/neutral/negative) across all Classifications (so a sentence might be “positive Guidance” and “positive KPI” at the same time). The other NLP application is what we call “deflection.” We dug through the academic literature and identified a specific lexicon that has shown to lead to future negative corporate events. 

Looking at the Deflection summary view in the Tesla call, we see quite a bit of the usual enthusiasm and grand visions from CEO Elon Musk.

We see the recently appointed CFO discussing the challenges in the quarter-to-quarter business:

It was most interesting to us that the remarks by the outgoing CTO (a surprise departure) were also picking up as deflection:

The final statement on the call by the CEO was also marked as deflection: will Tesla beat the industry economics?

To find out how Sentieo can help with your process, please get in touch

New call-to-action

Disrupting Transcripts: Short Video Example on UPS

Last week, just in time for earnings season, we released Sentieo’s machine learning-based transcript Smart Summary™ feature. In short, we automated what we used to do as buyside analysts: reading through transcripts and classifying information.

This week, we’ve looked at the newest transcripts from Goldman Sachs and Haliburton in detail. Yesterday, we shared an in-depth video discussing Whirlpool: from redlining the 8-K, to looking at the stock price action post-call, to its Smart Summary™. 

Today’s video is shorter, as we only focus on the Smart Summary™ aspect and we discuss UPS. We were able to extract the important information in about 2 minutes.

The Smart Summary™ is one of two major ML-based product releases we had this month: take a look at our synonym suggestion tool as well. Your searches can be a lot more productive with this dynamic addition to our existing large synonym/acronym dictionary.

If you are ready to test out our platform, please get in touch.

New call-to-action

Disrupting Transcripts: A Smart Summary™ Video Tutorial

This week we’re showcasing our new, machine learning-based transcript Smart Summary™, an entirely new way of reading — and more importantly, summarizing — transcripts

Yesterday, we looked at Halliburton’s transcript Smart Summary™In today’s post, we’re sharing a video that covers Whirlpool (NYSE:WHR), a “classic” industrials company. We review the earnings 8-K, discuss the positive after-market reaction to the guidance increase, and then we dive into the transcript to see why the stock actually declined after the call. 

To try using Smart Summary™ in your own research workflow, sign up for a custom demo.

Disrupting Transcripts: How We Used Sentieo’s Transcript Smart Summary™ to Review the Halliburton (HAL) Call

Oilfield services provider Halliburton (NYSE:HAL) is the first installment in this week’s Smart Summary™ showcase. We are applying our newly released machine learning-based tool to improve our own reading while saving time. Below, we will highlight how we picked out the critical pieces of information on the call in just a handful of minutes. 

If you haven’t read this feature’s announcement post, here’s a recap. Smart Summary™ is a one-click application of machine learning that “slices” transcripts in several ways. One of these slices is a broad Classification, like “Guidance” or “KPIs. (See 1 below).

The second slice (2) is based off NLP-powered sentiment (green and red labels). The third application is based on surfaced Key Terms: products, brands, and geographies (3). The fourth and most innovative application is the removal of the chronological flow all together; sentences are sorted in a table view mode, and can be ranked by “signal strength.”(4) 

(1) (2) This is what the clickable Classifications look like for HAL’s transcript:

(3) Top NLP-surfaced Key Terms are clickable as well.

(4) Clicking the Table Icon in the upper right will open the sortable sentence Table View. (Click on image to zoom in).

With this in mind, here is what we are seeing as key pieces of information in HAL’s transcript.

Free Cash Flow has been and will continue to be positive. This is really good news given the FCF challenges in the energy industry:

Directly related to FCF, HAL is cutting back on capex and is focusing on margins:

Increased activity in several regions led revenue growth in the quarter, while the overall outlook is optimistic:

Switching from the full transcript view to the summary view for the Guidance classification, we see the summary at the end of the call: guarded optimism and focus on margins and capex. 

We complete the final review of the transcript using Table View: the non-chronological way to extract information from transcripts. We can see how key sentences on the topics highlighted above are picked up by NLP. Readers can sort sentences along several categories. 

In terms of Guidance “loading,” we see Free Cash Flow ranked highly multiple times:

Ranked purely on sentiment, we can see that the overall business did well in the quarter across several geographies:

Ranking for Products and Markets, we see new product discussions as well as the pricing signals.

Investors can also easily pick up the negatives in the results and in the outlook: large legacy frac fleet, soft pricing seen by competitors, some segments might see revenue declines. 

Reading non-chronologically does not mean reading out of context. Simply click on the Context link to see the phrase within context. 

The Table View is also available for the NLP-surfaced Key Terms. In one glance we can see whether these terms are used more or less quarter-over-quarter, and by whom (analysts vs. management). We see how prominently “North America,” “capex,” and “Free Cash Flow” are featured. An added time-saving convenience is that these Key Terms are click-through: clicking on one will search all HAL transcripts for that term, so you can get immediate historical context in seconds. 

Here we have clicked on Capex, and we can see highlights in the most recent transcript: note how Sentieo’s synonym search picks up various forms of the term.

To learn more about how using the latest ML-based tools can help you and your team be more productive, please get in touch with us

 

New call-to-action

Introducing Sentieo’s New Document Search User Interface

Sentieo users are now able to try the beta release of our brand new Document Search user interface (UI). While it may seem familiar, the UI has been rebuilt using modern tech such as React. This new build enables significantly less memory consumption and greater responsiveness across the board. Plus, we’ve extended Sentieo’s sentiment and natural language processing capabilities to include a host of new AI-powered features detailed here.

AI-Powered Features

 

Smart Summary™

With Sentieo’s new, ML-driven transcript Smart Summary™, we enable analysts to pick up the important information across multiple classifications, such as guidance and KPIs.

To trigger Smart Summary™, users can simply click the “brain” icon on any transcript. To learn more about Smart Summary™, read this post.

 

 

Query Autocomplete

Our data science team has built an algorithm to identify key terms and topics for all companies within our database. Similar to a Google search, when a user starts typing inside of the query box, Sentieo will automatically populate suggested terms for you based on actual terms used by the company. This makes it much easier to find exactly the keywords you are looking for. Autocomplete intelligently populates based on which tickers and watchlists you have selected in the ticker box.

 

Suggested Synonyms

We are very excited to announce a giant leap forward for our Synonyms system. We have been using an NLP + machine learning-based algorithm for the last couple years to help us internally define and expand our deep synonym library. We are now very excited to open up that system to Sentieo users with our new Suggested Synonyms feature.

Using Suggested Synonyms, Sentieo is able to parse your query and automatically suggest possible synonyms to expand your search. While all of these synonyms may not be pertinent to your search, the function lets you quickly decide which you would like to add to your search. Just click the suggested synonym and it will automatically be added to your search query:

General Document Search Improvements

The Sources Selector

When we looked at usage analytics, we realized that the Document Type filter was the most used filter by a large margin. To make this filter more accessible and easier to use, we’ve made it a part of the main search interface. Additionally, we have renamed the filter to “Sources” to more accurately represent the functionality.

We have also added a quick “only” selector to the right of each source that lets users quickly focus on a single source. 

Additionally, click the > icon at the far right to load up the more granular filters:

All additional filtering is now available with the Filter Button:

 

Configure and Control Your Results Pane

We’ve added the ability for users to easily resize the results panel. Simply drag the divider or use the maximize/minimize button. As you expand, the application will automatically switch to a table view when enough space is present. 

While in the table view, you can also control what columns are visible. Currently, the list of options is limited but the Sentieo team will be adding to this over the coming months. 

For smaller screens, we’ve also added the option to toggle between split pane and results-only views.

 

Company Summary

If you run a search with a ticker but without a query, the Sentieo initial results page shows you all company documents, as well as a company summary and share price performance. We have taken some key metrics and links out of our EDT Summary and provided them within Document Search. Additionally, users can see a historical stock price graph in the company summary.

 

Improved Document Viewer

We’ve made serious upgrades to our Document Viewer as well. You will notice faster load times for documents across the board. Additionally, we have relocated the highlights and the index panel to the right of the document.

We have also relocated the Redlining function to the top right of the document viewer.

 

To try the new Document Search interface for yourself, please log in, or get in touch!

 

New call-to-action