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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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.

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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!

 

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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

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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.

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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

 

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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!

 

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Disrupting Transcripts: Applying Sentieo’s Smart Summary™ During Earnings Season

After last week’s release of Sentieo’s machine learning-based Smart Summary™ for transcriptsthis week we will be highlighting exactly how to use this powerful new feature to ensure that you don’t miss important information this earnings season, and that you also save time.  

The Smart Summary™ tool classifies a document’s language based on several categories such as Guidance, KPIs, and company-specific key terms (like product names) into a convenient, time-saving format. Further, it applies an NLP sentiment filter to highlight positive and negative language. 

Smart Summary™ is the latest machine learning and natural language processing-based tool released by Sentieo this year. We’ve also released a dynamic synonym suggestions feature for Document Search based on our own, internal synonym search tool. Additionally, we added company-specific autocomplete suggestions to our search. And earlier this year, we rolled out Table Explorer, a flexible and auditable tool that identifies, chains and visualizes tables in SEC filings. 

During the next few days, we will be selecting and publishing our highlights from current transcripts to show how effective Smart Summary™ can be in your research workflow. (Stay tuned).

For example, if we look at the transcript from the Goldman Sachs call last week (below), we can pick up essential corporate and macro information (color-coded as needed), under the Financials and KPIs classification. Scrolling down in the Smart Summary™, we see record assets under supervision: good news in green, versus slower Chinese GDP growth in red, a couple of paragraphs down. The full context is right there as well. 

In addition to viewing by Classification group, users can quickly scroll through all positive and negative highlights, regardless of Classification. 

In the Key Terms field, we catch up on Marcus, Goldman’s growing consumer banking franchise. We can see a mix of positive and neutral statements. 

For the next few days, the blog will be highlighting exactly how we’re applying this powerful new functionality. To try it yourself, please get in touch

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Disrupting Transcripts: Introducing Sentieo’s Smart Summary™

Just in time for earnings season, Sentieo is releasing Smart Summary™, a whole new way to read transcripts. 

We all know the problem that analysts and portfolio managers face four times per year: multiple companies reporting on the same day, which creates impossible scheduling conflicts, work overload, and forced prioritization, which then leads to costly missed information.

Analysts might dial into one or two consecutive high priority calls, and then go on to read another four to eight transcripts later in the day, depending on coverage. These transcripts are always read chronologically, starting with the boilerplate language on top, and ending with the analyst questions. 

Until now. 

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, while skipping over the “great quarter, guys!” parts. The segments based on these classifications can either be seen highlighted in the transcript itself or extracted, with context, in a special field. The text is also run through an NLP screen and color-coded in green or red, if needed, with enabled clickable user ratings that will continue to improve the accuracy of the learning system. In addition, Smart Summary offers keyword extractions, enabling readers to view text related to these surfaced key terms.

These features empower analysts to get the essential information in minutes, saving valuable time during the busiest periods of the year. The benefit of the ML-driven Smart Summaries does not end with faster information processing or expanded coverage; it also ensures that analysts are not missing important information on their last few transcripts at the end of the workday. 

Below we are looking at Facebook’s latest quarterly call transcript, and scrolling down to the Guidance classification text:

Below we are looking at the same Facebook transcript, but reviewing the Whatsapp key term text:

Another aspect of Smart Summary is “Table View,” which enables active sorting of sentences by the Classification score (ie. “which sentences have the most guidance language?”). This adds another layer of improved accuracy and productivity to the analyst workflow. 

The Table View is also available for surfaced keywords. Analysts can see useful statistics, like whether a keyword is new, or whether it got dropped. They can also see whether usage, by either management or analysts, is going up or down. This type of quantitative insight for keywords was previously so labor-intensive that it was rarely done, and only for one or two key terms at most.

 

How did we build Smart Summary™?

We spent countless hours training a machine learning model on a very large number of transcripts across all industries, effectively automating what an analyst would do. Once the model was up and running, we spent a lot of time on corrections and fine-tuning for this official release. 

Smart Summary™ is a blockbuster new addition to the Sentieo platform that serves our overarching mission: augmenting human decision-making through the latest technological tools. The release is a part of the latest ML/NLP update to our industry-leading Document Search, which now also includes the surfacing of synonym suggestions based on a machine learning tool trained on millions of corporate documents. (We will discuss this in an upcoming blog post).

 

To see how Sentieo’s complete workflow solution can work for you, please get in touch

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