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== Executive Summary == Founded in Cambridge (UK) in 2013, Darktrace is a provider of AI-led threat detection and response security offerings covering on-premises network, cloud, SaaS, email, endpoints and OT (Operational Technology) environments. Further, Darktrace will roll out offerings tackling preventative security and remediation post attacks as part of its continuous AI security loop. According to Darktrace, its cyber AI platform does not rely on a historical signatures-based detection / rules-based response playbook; instead, its technology aims to learn the ‘patterns of life’ for an enterprise, creating a constantly evolving baseline for ‘normal’ behaviour, and detects and responds to deviations from the normal. Darktrace competes in market segments characterized by relatively low barriers to entry and high competition. Today, Darktrace leads the Network Detection and Response (NDR) market and has seen good early success with its email product; however, against the backdrop of '''1)''' high competition and potential commoditization of security solutions addressing similar use-cases, '''2)''' relatively low platform lock-in and '''3)''' growing enterprise awareness of competing vendors, JP Morgan expects customer acquisition and retention to become more challenging for Darktrace, going forward. With ARR (annualized recurring revenue) growth tied to new customer acquisition, JP Morgan believes that higher customer acquisition and retention costs are likely to challenge the company’s ability to deliver profitable growth. This will reflect in Darktrace’s valuation compared to its peer group, in JP Morgan's view. '''Accordingly, JP Morgan initiates coverage on Darktrace with a Dec-23 price target of 400p.''' JP Morgan's price target is based on 5.5x ’23E EV/sales (calendarized), ~35% discount to the peer group (comprising a targeted list of product-led threat detection and response and cyber exposure management/user behaviour analytics vendors). === Shifting the focus to profitability === Darktrace has delivered ‘beat and raise’ results in its short reporting history since IPO in Apr-21. In the near term, JP Morgan expects demand for AI-led detection and response offerings to remain high – this coupled with Darktrace’s brand awareness (a function of its high marketing spend), investment in additional salesforce hiring and the roll-out of the new ‘Prevent’ product offering should translate to healthy new customer acquisition and thus ARR growth, in JP Morgan's view. That said, unless the quality and stickiness of the customer base acquired is high, focus on new customer acquisition will not translate to sustainable profitable growth, in JP Morgan's view, even though the company may deliver healthy growth in the near term. In JP Morgan's view, Darktrace needs to demonstrate a decoupling between ARR growth and new customer acquisition & investment in sales headcount – this will be a function of a sustained increase in average contract ARR per new customer and continued improvement in net ARR retention rate (a function of lower churn and higher upsell/cross-sell). Sustained improvement in these metrics would allow the market to be more convinced of the company’s competitive positioning and moat. JP Morgan's estimates for 2022/23/24 revenue stand 1%/2%/5% higher than Bloomberg consensus estimates; JP Morgan expects the company to deliver adjusted EBITDA margin at the high end of its guidance range in 2022; however, JP Morgan's estimates for 2023/24 adjusted EBITDA stand 22%/4% below consensus estimates. {| class="wikitable" |+Table 1: Darktrace Revenue and adjusted EBITDA: FY22-24E - JPMe vs. consensus<ref>Source: J.P. Morgan estimates, Bloomberg Finance L.P.</ref> !FY ends in Jun ! colspan="3" |FY22 ! colspan="3" |FY23 ! colspan="3" |FY24 |- !$ (m) !JPMe !Cons. !Diff. (%) !JPMe !Cons. !Diff. (%) !JPMe !Cons. !Diff. (%) |- |Revenue |415.5 |409.6 |1.4% |548.1 |536.5 |2.2% |701.5 |670.2 |4.7% |- |Adj. EBITDA |49.2 |48.6 |1.3% |47.2 |60.4 |<nowiki>-21.9%</nowiki> |81.0 |84.6 |<nowiki>-4.3%</nowiki> |- |margin (%) |11.8% |11.9% |<nowiki>-2bps</nowiki> |8.6% |11.3% |<nowiki>-265bps</nowiki> |11.5% |12.6% |<nowiki>-108bps</nowiki> |} === Market for AI-led threat detection and response security offerings marked by low barriers to entry and high competition === Today, Darktrace leads the Network Detection and Response (NDR) market and has seen good early success with its email product. However, with relatively low barriers to entry and increased competition, JP Morgan sees a risk of commoditization of solutions targeting the use-cases addressed by Darktrace. A growing list of vendors that seek to combine different point security solution offerings into an eXtended Detection and Response (XDR) fabric will only increase the competitive intensity for vendors such as Darktrace, whose offerings are a complement (rather than a replacement) for other point security tools such as Endpoint Detection and Response (EDR), in JP Morgan's view. In addition, JP Morgan believes that there is a real competitive threat from public cloud vendors such as Microsoft, Amazon and Google making a big push into proactive threat detection and response solutions for cloud traffic and email as enterprise workloads transition to the cloud. === ARR growth tied to new customer acquisition === A significant portion of Darktrace’s total addressable market opportunity depends on the company’s ability to grow its ARR independent of new customer acquisition. However, despite reporting higher platform adoption stats in the last couple of years, the company has not reported any meaningful uptick in average contract ARR (Dec- 21 level approx. flat vs. Jun-19). This is partly explained by Darktrace’s focus on acquiring new customers, SMB/mid-market skew in customer base and impact from new product launches. It remains to be seen whether Darktrace can decouple ARR growth from new customer acquisition, which will only get tougher with high competition and growing enterprise awareness of competing vendors. Unless the quality and stickiness of the customer base acquired is high, focus on new customer acquisition will not translate to sustainable profitable growth, in JP Morgan's view, even though the company may deliver healthy growth in the near term. === Risks to profitability === With low barriers to entry and high competition in the markets Darktrace operates in, JP Morgan believes that customer acquisition and retention will get tougher going forward. This may lead to higher customer acquisition costs and prompt Darktrace to increase investments in existing and new product development. While Darktrace may report healthy near-term growth, the eventual success of the company will be determined based on how the company balances growth and profitability. Assessing this development through the lens of ‘Rule of 40’ (revenue growth + FCF margin) is a good indicator of the progress the company is making to sustain profitable growth. JP Morgan expects the sum of revenue growth and FCF margin to dip and remain below 40% over the next couple of years. Eventually, this is likely to reflect in Darktrace’s valuation compared to other cybersecurity peers that consistently beat this 40% benchmark, in JP Morgan's view. === Risks to JP Morgan's view === * Competition risks may take time to play out: While JP Morgan believes that the market for AI-led threat detection and response solutions remains highly competitive, Darktrace’s brand awareness (supported by high marketing spend) may continue to support new customer acquisition without incurring higher costs. XDR vendor solutions may take time to mature; in addition, enterprises may likely be wary of getting locked-in to any particular XDR vendor’s ecosystem, potentially diluting the competitive threat from XDR vendors. Mid-term competitive threat from public cloud vendors may not materialize within a reasonable timeframe. * Broader platform adoption across larger enterprise customers: Darktrace’s customer base is skewed towards SMB/mid-market enterprises (~85% of customers generate sub-$100k ARR); however, growing adoption in larger enterprises and the subsequent shift in customer mix, may drive higher average contract ARR per customer and lower gross ARR churn – factors that may help the company scale up profitability over the coming years. * A '''sustained decoupling between ARR growth and new customer acquisition''' / investment in new sales headcount might prompt us to revisit JP Morgan's UW thesis, as this would put the company on path towards sustainable profitable growth. * Regulatory/compliance laws (or cyber insurance requirements) mandating the use of AI-led threat detection and response solutions could boost demand for Darktrace’s offering, potentially lowering new customer acquisition costs. * M&A: Darktrace may appear a candidate for acquisition by larger cybersecurity vendors looking to acquire AI-driven detection and response solutions. This expectation may offer downside support to Darktrace’s share price, in JP Morgan's view. * Near-term support from healthy ‘beat and raise’ results: JP Morgan's concerns on profitable growth may not play out over the next few quarters, which may result in the company delivering consistent strong performance in the near-term. === Summary of key financials === {| class="wikitable" |+Table 2: Darktrace: Summary of key financial items<ref>Source: Company data, J.P. Morgan estimates; *includes share-based comp and associated employer tax charges.</ref> |$ (m), FY ends in Jun |2018 |2019 |2020 |2021 |2022E |2023E |2024E |- |Revenue |79.4 |137.0 |199.1 |281.3 |415.5 |548.1 |701.5 |- |YoY | |72.5% |45.3% |41.3% |47.7% |31.9% |28.0% |- |Gross profit |71.2 |124.8 |181.6 |252.9 |369.7 |483.5 |613.9 |- |as % of revenue |89.6% |91.1% |91.2% |89.9% |89.0% |88.2% |87.5% |- |Key opex items as % of revenue* | | | | | | | |- |Sales & marketing |114.7% |95.3% |81.9% |67.2% |63.2% |63.8% |62.0% |- |R&D |9.5% |7.1% |6.0% |10.2% |10.5% |12.9% |13.4% |- |G&A |15.8% |14.8% |13.5% |20.1% |23.2% |22.8% |21.0% |- |Operating profit |(40.6) |(36.2) |(24.9) |(38.5) |(32.5) |(62.4) |(62.1) |- |as % of revenue |<nowiki>-51.1%</nowiki> |<nowiki>-26.4%</nowiki> |<nowiki>-12.5%</nowiki> |<nowiki>-13.7%</nowiki> |<nowiki>-7.8%</nowiki> |<nowiki>-11.4%</nowiki> |<nowiki>-8.9%</nowiki> |- |Adjusted EBIT |(37.3) |(28.9) |(14.6) |0.1 |11.5 |(4.2) |12.1 |- |as % of revenue |<nowiki>-47.0%</nowiki> |<nowiki>-21.1%</nowiki> |<nowiki>-7.3%</nowiki> |0.0% |2.8% |<nowiki>-0.8%</nowiki> |1.7% |- |Adjusted EBITDA |(27.0) |(11.3) |8.9 |29.7 |49.2 |47.2 |81.0 |- |as % of revenue |<nowiki>-34.0%</nowiki> |<nowiki>-8.2%</nowiki> |4.5% |10.6% |11.8% |8.6% |11.5% |- |Net cash |12.0 |33.1 |18.4 |307.1 |328.0 |348.6 |388.3 |- |FCF |(35.1) |(9.0) |(3.8) |34.6 |44.8 |33.1 |52.3 |- |LTM FCF as % of LTM revenue |<nowiki>-44.2%</nowiki> |<nowiki>-6.5%</nowiki> |<nowiki>-1.9%</nowiki> |12.3% |10.8% |6.0% |7.5% |- |LTM revenue growth + FCF margin (%) | |66.0% |43.4% |53.6% |58.4% |38.0% |35.4% |}
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